Abstract: Recognizing age and gender based on facial images is one of the current hot spots in artificial intelligence research. This paper proposes a hybrid model that integrates Convolution Neural Network (CNN), Squeeze-Excitation Network (SENet) and Extreme Learning Machine (ELM). The con-volutional layer in the model is used to extract facial features from the face image, the SEnet layer is used to optimize the features extracted by the convolutional layer, and the error minimization extreme learning machine (EM-ELM) is used as a classifier to realize the age and gender recognition of facial images. Compared with the existing popular models, the proposed model adopts the CNN+SENet architecture so that it can extract more representative and optimal feature maps from facial images, and the extremely fast calculation of EM-ELM makes the model faster and more efficient. Experimental results on multiple unrestricted face datasets show that the proposed model has higher recognition accuracy and speed than other recent related models based on deep learning. 

Key words: convolutional neural network, extreme learning machine, SENet network, age classification, gender classification