Neural network based cognitive approaches from face perception with human performance benchmark
Abstract
Artificial neural network models are able to achieve great performance at numerous computationally challenging tasks like face recognition. It is of significant importance to explore the difference between neural network models and human brains in terms of computational mechanism. This issue has become an experimental focus for some researchers in recent studies, and it is believed that using human behavior to understand neural network models can address this issue. This paper compares the neural network model performance with human performance on a classic yet important task: judging the ethnicity of a given face. This study uses Caucasian and East Asian faces to train 4 neural networks including AlexNet, VGG11, VGG13, and VGG16. Then, the ethnicity judgments of the neural networks are compared with human data using classical psychophysical methods by fitting psychometric curves. The results suggest that VGG11, followed by VGG16, shows a similar response pattern as humans, while simpler AlexNet and more complex VGG13 do not resemble human performance. Thus, this paper explores a new paradigm to compare neural networks and human brains.
- Publication:
-
Pattern Recognition Letters
- Pub Date:
- August 2024
- DOI:
- 10.1016/j.patrec.2024.06.024
- Bibcode:
- 2024PaReL.184..155C
- Keywords:
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- Neural network;
- Class activate map;
- Ethnicity identification;
- Face perception;
- Cognitive science