Hand Gesture Recognition Systems: A Survey
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paper_title: A Survey of Hand Posture and Gesture Recognition Techniques and Technology

paper_content:

This paper surveys the use of hand postures and gestures as a mechanism for interaction with computers, describing both the various techniques for performing accurate recognition and the technological aspects inherent to posture- and gesture-based interaction. First, the technological requirements and limitations for using hand postures and gestures are described by discussing both glove-based and vision-based recognition systems along with advantages and disadvantages of each. Second, the various types of techniques used in recognizing hand postures and gestures are compared and contrasted. Third, the applications that have used hand posture and gesture interfaces are examined. The survey concludes with a summary and a discussion of future research directions.
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paper_title: A Fast Algorithm for Vision-Based Hand Gesture Recognition for Robot Control

paper_content:

We propose a fast algorithm for automatically recognizing a limited set of gestures from hand images for a robot control application. Hand gesture recognition is a challenging problem in its general form. We consider a fixed set of manual commands and a reasonably structured environment, and develop a simple, yet effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture. The algorithm is invariant to translation, rotation, and scale of the hand. We demonstrate the effectiveness of the technique on real imagery.
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paper_title: 3-Draw: a three dimensional computer aided design tool

paper_content:

3-Draw is a tool for computer-aided design targeted at the early concept-forming stages of design. 3-Draw is intended to preserve the benefits of paper and pencil while taking advantage of the computer to develop, manipulate, and display 3-D representations of objects interactively. Designers using 3-Draw sketch out their initial ideas directly in three dimensions, using the computer to display objects in perspective and undergoing real-time motion. The hardware consists of two six-degrees-of-freedom sensors and a Silicon Graphics IRIS-4D/70GT graphics workstation. One sensor is configured to control an object's position and orientation. The other sensor is a multiconfigurable 3-D drawing/editing tool. Since the sketching is done directly in 3-D, the computer can generate limitless perspective views of models and the designer does not need to perform the image projection mentally. >
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paper_title: Hand modeling, analysis and recognition

paper_content:

Analyzing hand gestures is a comprehensive task involving motion modeling, motion analysis, pattern recognition, machine learning and even psycholinguistic studies. A comprehensive review of various techniques in hand modeling, analysis, and recognition is needed. Due to the multidisciplinary nature of this research topic, we cannot include all the works in the literature. Rather than function as a thorough review paper, this article serves as a tutorial to this research topic. We study 3-D hand models, various articulated motion analysis methods, and gesture recognition techniques employed in current research. We conclude with some thoughts about future research directions. We also include some of our own research results, some of which are shown as examples.
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paper_title: Vision-based hand pose estimation: A review

paper_content:

Direct use of the hand as an input device is an attractive method for providing natural human-computer interaction (HCI). Currently, the only technology that satisfies the advanced requirements of hand-based input for HCI is glove-based sensing. This technology, however, has several drawbacks including that it hinders the ease and naturalness with which the user can interact with the computer-controlled environment, and it requires long calibration and setup procedures. Computer vision (CV) has the potential to provide more natural, non-contact solutions. As a result, there have been considerable research efforts to use the hand as an input device for HCI. In particular, two types of research directions have emerged. One is based on gesture classification and aims to extract high-level abstract information corresponding to motion patterns or postures of the hand. The second is based on pose estimation systems and aims to capture the real 3D motion of the hand. This paper presents a literature review on the latter research direction, which is a very challenging problem in the context of HCI.
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paper_title: Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems

paper_content:

We propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems. Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique. Our gesture recognition system is particularly advantageous for human-computer interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.
---
paper_title: Dynamic hand gesture recognition using hidden Markov models

paper_content:

Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex singlehand gestures. Gesture images are gained by a common web camera. Skin color is used to segment hand area from the image to form a hand image sequence. Then we put forward a state-based spotting algorithm to split continuous gestures. After that, feature extraction is executed on each gesture. Features used in the system contain hand position, velocity, size, and shape. We raise a data aligning algorithm to align feature vector sequences for training. Then an HMM is trained alone for each gesture. The recognition results demonstrate that our methods are effective and accurate.
---
paper_title: Hand Gesture Modeling and Recognition using Geometric Features: A Review

paper_content:

Abstract — The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.
---
paper_title: Fingertip Detection for Hand Pose Recognition

paper_content:

In this paper, a novel algorithm is proposed for fingertip detection and finger type recognition. The algorithm is applied for locating fingertips in hand region extracted by Bayesian rule based skin color segmentation. Morphological operations are performed in the segmented hand region by observing key geometric features. A probabilistic modeling of the geometric features of finger movement has made the finger type recognition process significantly robust. Proposed method can be employed in a variety of applications like sign language recognition and human robot interactions.
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paper_title: Hand Gesture Recognition Using Neural Networks.

paper_content:

Abstract : Gestural interfaces have the potential of enhancing control operations in numerous applications. For Air Force systems, machine-recognition of whole-hand gestures may be useful as an alternative controller, especially when conventional controls are less accessible. The objective of this effort was to explore the utility of a neural network-based approach to the recognition of whole-hand gestures. Using a fiber-optic instrumented glove, gesture data were collected for a set of static gestures drawn from the manual alphabet used by the deaf. Two types of neural networks (multilayer perceptron and Kohonen self-organizing feature map) were explored. Both showed promise, but the perceptron model was quicker to implement and classification is inherent in the model. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.
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paper_title: A static hand gesture recognition algorithm using k-mean based radial basis function neural network

paper_content:

The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.
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paper_title: Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review

paper_content:

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient "purposive" approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of human-computer interaction.
---
paper_title: Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review

paper_content:

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient "purposive" approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of human-computer interaction.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: Two-hand gesture recognition using coupled switching linear model

paper_content:

We present a method coupling multiple switching linear models. The coupled switching linear model is an interactive process of two switching linear models. Coupling is given through causal influence between their hidden discrete states. The parameters of this model are learned via the EM algorithm. Tracking is performed through the coupled-forward algorithm based on Kalman filtering and a collapsing method. A model with maximum likelihood is selected out of a few learned models during tracking. We demonstrate the application of the proposed model to tracking and recognizing two-hand gestures.
---
paper_title: Hand Gesture Modeling and Recognition using Geometric Features: A Review

paper_content:

Abstract — The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.
---
paper_title: Fingertip Detection for Hand Pose Recognition

paper_content:

In this paper, a novel algorithm is proposed for fingertip detection and finger type recognition. The algorithm is applied for locating fingertips in hand region extracted by Bayesian rule based skin color segmentation. Morphological operations are performed in the segmented hand region by observing key geometric features. A probabilistic modeling of the geometric features of finger movement has made the finger type recognition process significantly robust. Proposed method can be employed in a variety of applications like sign language recognition and human robot interactions.
---
paper_title: Hand gesture recognition using morphological principal component analysis and an improved CombNET-II

paper_content:

A new neural network structure dedicated to time series recognition, T-CombNET, is presented. The model is developed from a large scale neural network CombNet-II, designed to deal with a very large vocabulary for character recognition. Our specific modifications of the original CombNet-II model allows it to do temporal analysis, and to be used in a large set of human movement recognition systems. This paper also presents a feature extraction method based on morphological principal component analysis that completely describes a hand gesture in 2-dimensional time varying vector. The proposed feature extraction method along with the T-CombNET structure were then used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a superior recognition rate of 99.4% in the gesture recognition experiments when compared to different neural network structures like multi-layer perceptron, learning vector quantization (LVQ), Elman and Jordan partially recurrent neural networks, CombNET-II and the proposed T-CombNET structure.
---
paper_title: Dynamic hand gesture recognition using hidden Markov models

paper_content:

Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex singlehand gestures. Gesture images are gained by a common web camera. Skin color is used to segment hand area from the image to form a hand image sequence. Then we put forward a state-based spotting algorithm to split continuous gestures. After that, feature extraction is executed on each gesture. Features used in the system contain hand position, velocity, size, and shape. We raise a data aligning algorithm to align feature vector sequences for training. Then an HMM is trained alone for each gesture. The recognition results demonstrate that our methods are effective and accurate.
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paper_title: A Study on Hand Gesture Recognition Technique

paper_content:

Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy. ::: The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation. ::: Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. ::: These extracted features are applied as input to classifier. Linear classifier discriminates the images based on dissimilarity between two images. Multi Class Support Vector Machine (MCSVM) and Least Square Support Vector Machine (LSSVM) is also implemented for the classification purpose. Experimental result shows that 94.2% recognition accuracy is achieved by using linear classifier and 98.6% recognition accuracy is achieved using Multiclass Support Vector machine classifier. Least Square Support Vector Machine (LSSVM) classifier is also used for classification purpose and shows 99.2% recognition accuracy.
---
paper_title: Hand gesture recognition using morphological principal component analysis and an improved CombNET-II

paper_content:

A new neural network structure dedicated to time series recognition, T-CombNET, is presented. The model is developed from a large scale neural network CombNet-II, designed to deal with a very large vocabulary for character recognition. Our specific modifications of the original CombNet-II model allows it to do temporal analysis, and to be used in a large set of human movement recognition systems. This paper also presents a feature extraction method based on morphological principal component analysis that completely describes a hand gesture in 2-dimensional time varying vector. The proposed feature extraction method along with the T-CombNET structure were then used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a superior recognition rate of 99.4% in the gesture recognition experiments when compared to different neural network structures like multi-layer perceptron, learning vector quantization (LVQ), Elman and Jordan partially recurrent neural networks, CombNET-II and the proposed T-CombNET structure.
---
paper_title: Hand Gesture Modeling and Recognition using Geometric Features: A Review

paper_content:

Abstract — The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.
---
paper_title: Hand Gesture Modeling and Recognition using Geometric Features: A Review

paper_content:

Abstract — The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.
---
paper_title: Edge Detection Techniques - An Overview

paper_content:

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge definition, properties of detectors, the methodology of edge detection, the mutual influence between edges and detectors, and existing edge detectors and their implementation.
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paper_title: Edge Detection by Morphological Operations and Fuzzy Reasoning

paper_content:

This article presents a new edge detection method for gray level images by morphological operators and fuzzy reasoning. The method concerns edge gradient magnitude in images by various morphological operators as fuzzy membership functions. A fuzzy reasoning acrobatic coupled with few rules was then acquired for edge sharpening. The whereabouts merit of good edge detection was compared and the experimental results show the efficiency of sharpening and edge detection.
---
paper_title: Hand Gesture Modeling and Recognition using Geometric Features: A Review

paper_content:

Abstract — The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.
---
paper_title: REAL TIME HAND GESTURE RECOGNITION USING SIFT

paper_content:

The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.
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paper_title: Detection and tracking of pianist hands and fingers

paper_content:

Current MIDI recording and transmitting technology allows teachers to teach piano playing remotely (or off-line): a teacher plays a MIDI-keyboard at one place and a student observes the played piano keys on another MIDI-keyboard at another place. What this technology does not allow is to see how the piano keys are played, namely: which hand and finger was used to play a key. In this paper we present a video recognition tool that makes it possible to provide this information. A video-camera is mounted on top of the piano keyboard and video recognition techniques are then used to calibrate piano image with MIDI sound, then to detect and track pianist hands and then to annotate the fingers that play the piano. The result of the obtained video annotation of piano playing can then be shown on a computer screen for further perusal by a piano teacher or a student.
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paper_title: Hand gesture recognition using a neural network shape fitting technique

paper_content:

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success.
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paper_title: Detection of Fingertips in Human Hand Movement Sequences

paper_content:

This paper presents an hierarchical approach with neural networks to locate the positions of the fingertips in grey-scale images of human hands. The first chapters introduce and sum up the research done in this area. Afterwards, our hierarchical approach and the preprocessing of the grey-scale images are described. A low-dimensional encoding of the images is done by the means of Gabor-Filters and a special kind of artificial neural net, the LLM-net, is employed to find the positions of the fingertips. The capabilities of the system are demonstrated on three tasks: locating the tip of the forefinger and of the thumb, finding the pointing-direction regardless of the operator’s pointing style, and detecting all 5 fingertips in hand movement sequences. The system is able to perform these tasks even when the fingertips are in an area with low contrast.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems

paper_content:

We propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems. Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique. Our gesture recognition system is particularly advantageous for human-computer interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: Fingertip Detection for Hand Pose Recognition

paper_content:

In this paper, a novel algorithm is proposed for fingertip detection and finger type recognition. The algorithm is applied for locating fingertips in hand region extracted by Bayesian rule based skin color segmentation. Morphological operations are performed in the segmented hand region by observing key geometric features. A probabilistic modeling of the geometric features of finger movement has made the finger type recognition process significantly robust. Proposed method can be employed in a variety of applications like sign language recognition and human robot interactions.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---
paper_title: A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

paper_content:

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
---

Title: Hand Gesture Recognition Systems: A Survey
Section 1: INTRODUCTION
Description 1: This section provides an overview of the significance of gestures in human communication, their historical context, and the various applications of hand gesture recognition systems.

Section 2: HAND MODELING FOR GESTURE RECOGNITION
Description 2: This section discusses the human hand's structure, degrees of freedom, and the different modeling techniques used for gesture recognition, including both 2D and 3D models.

Section 3: SYSTEM ARCHITECTURE
Description 3: This section outlines the phases of a hand gesture recognition system, including data acquisition, hand segmentation, pre-processing, feature extraction, and recognition.

Section 4: Gesture Modeling
Description 4: This section describes the steps involved in gesture modeling, including hand segmentation, noise removal, edge detection, and normalization.

Section 5: Hand Segmentation
Description 5: This section details various methods for hand segmentation, such as skin-based approaches, background subtraction, statistical models, and color normalization.

Section 6: Noise Removal
Description 6: This section explains different noise removal techniques, like salt and pepper noise filters, morphological erosion, and multidimensional mean methods.

Section 7: Edge Detection
Description 7: This section covers techniques for edge detection in images, including gradient-based and Laplacian-based methods.

Section 8: Normalization
Description 8: This section discusses normalization techniques aimed at feature space reduction, such as cropping, dimension unification, and significant features location.

Section 9: Feature Extraction
Description 9: This section covers the extraction of features crucial for hand gesture recognition, discussing model-based, view-based, and low-level features based approaches.

Section 10: Hand Gesture Recognition
Description 10: This section explains how gestures are recognized using machine learning techniques and classifiers, and examines supervised and unsupervised methods.

Section 11: Research Results
Description 11: This section provides a summary of research works in the domain, presenting a comparative study of backgrounds, segmentation techniques, features, and recognition methods used along with key criteria such as robustness, computational efficiency, user's tolerance, and scalability.

Section 12: CONCLUSION
Description 12: This section summarizes the findings of the survey, highlights the importance of hand gesture recognition, and discusses the current limitations and future research directions.

Section 13: ACKNOWLEDGMENTS
Description 13: This section acknowledges contributions from individuals and organizations that assisted in the research.