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Beyond Frontal Face Recognition
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AI
DOI：10.1109/ACCESS.2023.3258444.png)
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摘要
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Face recognition is one of the most researched subjects in computer vision. The attention it receives is due to the complexity of the problem. Face recognition models have to deal with a wide variety of intraclass variations such as pose variations, facial expressions, the effect of aging, and natural occlusion due to different illumination. This challenge is often referred to as pose-illumination-expression. Our brain performs face recognition efficiently because we process it holistically. Since achieving human-level accuracy in face recognition is the ultimate goal, we should ascertain whether a computational model mimicking this approach would better tackle this problem. In this research, we developed a computational learning model that closely mimics the way the human visual cortex performs face recognition. It decomposes the recognition task into two specialized sub-tasks. A generator performs the holistic step, followed by a classifier for the recognition step, together referred to as the holistic model. To deal with the pose variations problem, we introduced the use of calculated distance features known as configural information (CI), which correlate the frontal face with profile face. We compared the holistic model against two baseline models and the current state-of-the-art (or classical models). The experimental results show the holistic model outperforming the current state-of-the-art for the Multi-PIE dataset, by 2.14% and performed as expected for the Labeled Face in the Wild dataset. The ability of the holistic model to recognize a face in any orientation with high accuracy will have a tremendous impact on biometric authentication with liveness detection.
Keyword:
Face recognition
Feature extraction
Generators
Generative adversarial networks
Computational modeling
Lighting
Deep learning
Pose estimation
engineered feature
face recognition
holistic processing
invariant feature
pose-and-illumination-invariant feature
spoofing attack
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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