Enhancing Facial Classification and Recognition using 3D Facial Models and Deep Learning

TL;DR

This paper introduces a novel method combining 3D facial models with deep learning to significantly improve facial classification and recognition accuracy across various attributes.

Contribution

It presents a new approach integrating 3D facial data with ResNet deep learning architecture, achieving high accuracy in classification tasks.

Findings

100% individual classification accuracy

95.4% gender classification accuracy

83.5% expression classification accuracy

Abstract

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.