2023,
Brain Sciences.
DOI:
10.3390/brainsci13020296
Challenging the Classical View: Recognition of Identity and Expression as Integrated Processes
Emily Schwartz
, Kathryn O’Nell
, Rebecca Saxe
, Stefano Anzellotti
Abstract:
Recent neuroimaging evidence challenges the classical view that face identity and facial expression are processed by segregated neural pathways, showing that information about identity and expression are encoded within common brain regions. This article tests the hypothesis that integrated representations of identity and expression arise spontaneously within deep neural networks. A subset of the CelebA dataset is used to train a deep convolutional neural network (DCNN) to label face identity (chance = 0.06%, accuracy = 26.5%), and the FER2013 dataset is used to train a DCNN to label facial expression (chance = 14.2%, accuracy = 63.5%). The identity-trained and expression-trained networks each successfully transfer to labeling both face identity and facial expression on the Karolinska Directed Emotional Faces dataset. This study demonstrates that DCNNs trained to recognize face identity and DCNNs trained to recognize facial expression spontaneously develop representations of facial expression and face identity, respectively. Furthermore, a congruence coefficient analysis reveals that features distinguishing between identities and features distinguishing between expressions become increasingly orthogonal from layer to layer, suggesting that deep neural networks disentangle representational subspaces corresponding to different sources.
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2023-05-31 22:39:00
# paper:Challenging the Classical View: Recognition of Identity and Expression as Integrated Processes. https://doi.org/10.3390/ brainsci13020296 最近神经影像学的证据挑战了以往关于人脸信息特征和面部表情由不同神经通路分别加工处理的经典观点，而是认为身份和表情的信息在共同的脑区被编码。作者基于这一背景利用深度卷积神经网络分别对面孔身份和面孔表情的数据集进行了训练，结果发现各自训练后的神经网络不仅可以分别很好的解码身份/表情，同时对于解码未训练过的表情/身份时也有较好的表现。这一结果验证了上述假设。