关键词: 深度学习, 卷积神经网络, 年龄分类, 性别识别

Abstract: Automatic age and gender classification of face images is an important task for face analysis. However, there is currently no model of accurate age and gender classification for face images in real world. A deep Convolutional Neural Network（CNN） is proposed to complete the recognition task. The first layer of large-scale convolution filters is replaced by cascade 3×3 convolution filters. The last hidden layer is fully connected to both the average pooling and the fifth convolutional layers such that it sees multi-scale features（features in the fifth convolutional layer are more global than those in the average pooling layer）. Because of the use of Batch Normalization layer, a higher learning rate and a smaller Dropout ratio can be set. 1×1 convolution filters and global average pooling over feature maps are utilized instead of the fully convolution. The results show that the convolutional neural network proposed in this paper is superior to other state-of-the-art classification methods and achieves 89.8% accuracy on age classification, 93.3% accuracy on gender classification on Adience dataset.

Key words: deep learning, convolutional neural network, age classification, gender detection