Open AccessPickPatch: A Regularization Method for Deep Face Recognition
Author(s) -
Linjun Sun,
Wei He,
Xin Ning,
Weijun Li,
Yuan Shi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1487/1/012024
Subject(s) - convolutional neural network , regularization (linguistics) , artificial intelligence , computer science , face (sociological concept) , pattern recognition (psychology) , facial recognition system , deep learning , feature (linguistics) , artificial neural network , social science , linguistics , philosophy , sociology
This paper proposes a simple and efficient regularization method, called PickPatch, for face recognition based on a deep convolutional neural network (DCNN). The proposed method randomly selects patches in the input face image and the intermediate feature maps as the activation region according to facial landmarks during the training phase. PickPatch is an approximation method that trains a series of models for different face patches and provides a combined model. This strategy introduces the idea of model combination for multiple face patches but does not change the model structure, which is both simple and efficient. Experiments on the public LFW database demonstrate that the proposed regularization method based on current deep convolutional neural networks can achieve obvious improvements of face recognition accuracy.