Improved softmax loss for deep learning‐based face and expression recognition. Issue 4 (27th November 2019)

• Record Type: Journal Article
• Authors: Zhou, Jiancan; Jia, Xi; Shen, Linlin; Wen, Zhenkun; Ming, Zhong
• Is Part Of: Cognitive computation and systems. Volume 1:Issue 4(2019)
• Page Start: 97
• Page End: 102
• Publication Date: 2019-11-27
• Subjects: gradient methods -- image recognition -- computer vision -- face recognition -- learning (artificial intelligence) -- feature extraction -- convolutional neural nets; deep convolutional neural networks -- improved softmax loss -- deep CNN -- expression recognition -- deep learning‐based face recognition -- MNIST handwritten digits dataset -- CASIA‐WebFace -- FER2013 training set -- LFW dataset -- discriminative features; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2019.0010
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : In recent years, deep convolutional neural networks (CNN) have been widely used in computer vision and significantly improved the performance of image recognition tasks. Most works use softmax loss to supervise the training of CNN and then adopt the output of last layer as features. However, the discriminative capability of the softmax loss is limited. Here, the authors analyse and improve the softmax loss by manipulating the cosine value and input feature length. As the approach does not change the principle of the softmax loss, the network can easily be optimised by typical stochastic gradient descent. The MNIST handwritten digits dataset is employed to visualise the features learned by the improved softmax loss. The CASIA‐WebFace and FER2013 training set are adopted to train deep CNN for face and expression recognition, respectively. Results on both the LFW dataset and FER2013 test set show that the proposed softmax loss can learn more discriminative features and achieve better performance.