一种基于CNN-SE-ELM的年龄和性别识别模型

基于人脸图像识别年龄及性别是当前人工智能研究的热点之一.提出一种综合卷积神经网络CNN、挤压-激励网络SENet及极限学习机ELM的混合模型.模型中的卷积层用于从人脸图像中提取面部特征,SENet层用于优化卷积层提取的特征,误差最小化极限学习机(EM-ELM)用作分类器以实现面部图像的年龄及性别识别.与现有的流行模型相...

An age and gender recognition model based on CNN-SE-ELM

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.