Enhancing Facial Classification and Recognition using 3D Facial Models
  and Deep Learning
Houting Li Mengxuan Dong Lok Ming Lui
Accurate analysis and classification of facial attributes are essential in
various applications, from human-computer interaction to security systems. In
this work, a novel approach to enhance facial classification and recognition
tasks through the integration of 3D facial models with deep learning methods
was proposed. We extract the most useful information for various tasks using
the 3D Facial Model, leading to improved classification accuracy. Combining 3D
facial insights with ResNet architecture, our approach achieves notable
results: 100% individual classification, 95.4% gender classification, and 83.5%
expression classification accuracy. This method holds promise for advancing
facial analysis and recognition research.