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Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning
Alice J O'Toole1, Carlos D Castillo2
1School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas 75080, USA;
Annual Review of Vision Science
|August 4, 2021
Summary
Deep learning models excel at face recognition, offering new insights into how humans process faces. Computational approaches reveal structured face representations and challenge traditional views of visual processing and learning.
Area of Science:
- Computational Neuroscience
- Computer Vision
- Cognitive Science
Background:
- Deep learning models achieve human-level performance in face recognition.
- Computational approaches offer novel methods for studying human face processing.
Purpose of the Study:
- To review scientific progress in understanding human face processing via deep learning.
- To explore the implications of deep learning advances for vision science and neuroscience.
Main Methods:
- Reviewing computational approaches based on deep learning.
- Analyzing deep networks trained for face identification.
Main Results:
- Deep networks generate rich, structured face representations, impacting inverse optics theories.
- High-level visual face representations in deep learning are not reducible to interpretable features.
- Deep learning highlights multistep, interactive learning processes crucial for face skill development.
Conclusions:
- Deep learning provides a powerful framework for understanding human face processing.
- Advances in deep learning necessitate rethinking visual representations, neural coding, and learning theories.
- Computational models are essential for explaining complex human visual abilities like face recognition.
