Bees Local Phase Quantization Feature Selection for RGB-D Facial
  Expressions Recognition
Seyed Muhammad Hossein Mousavi Atiye Ilanloo
Feature selection could be defined as an optimization problem and solved by
bio-inspired algorithms. Bees Algorithm (BA) shows decent performance in
feature selection optimization tasks. On the other hand, Local Phase
Quantization (LPQ) is a frequency domain feature which has excellent
performance on Depth images. Here, after extracting LPQ features out of RGB
(colour) and Depth images from the Iranian Kinect Face Database (IKFDB), the
Bees feature selection algorithm applies to select the desired number of
features for final classification tasks. IKFDB is recorded with Kinect sensor
V.2 and contains colour and depth images for facial and facial
micro-expressions recognition purposes. Here five facial expressions of Anger,
Joy, Surprise, Disgust and Fear are used for final validation. The proposed
Bees LPQ method is compared with Particle Swarm Optimization (PSO) LPQ, PCA
LPQ, Lasso LPQ, and just LPQ features for classification tasks with Support
Vector Machines (SVM), K-Nearest Neighbourhood (KNN), Shallow Neural Network
and Ensemble Subspace KNN. Returned results, show a decent performance of the
proposed algorithm (99 % accuracy) in comparison with others.