关键词: 损失函数, L2范数, 多重范数, 人脸识别

Abstract:

To solve the problem of slow convergence after defining loss function as Cosine Margin Loss Function（Cosineface） in convolutional neural network and the defect of measuring feature similarity with L2 norm in practical use, a variable slope Cosine Margin Loss Function （Kcosine） and a multiple-norm method calculating feature similarity are proposed. By adding the cosine slope factor on the basis of cosine margin loss function, intra-class constraints increase with the increase of cosine value, and the intra-class distance is reduced explicitly. At the same time, the face feature similarity vector is constructed by using L2 norm and L∞ norm, and the classification is realized by Support Vector Machine（SVM） so that modifying instability of L2 norm space measurement. The 1∶1 validation experiments on LFW and Agedb databases show that the improved loss function not only accelerates the convergence speed of training, but also reduces the intra-class distance by more than 15%. At the same time, by using multiple-norm features instead of L2 norm, the average recognition rate can be increased by about 0.1%, and the standard deviation of recognition rate can also be reduced.

Key words: loss function, L2-norm, multiple-norm, face recognition