基于改进AlexNet的人脸表情识别

Facial Expression Recognition Based on Improved AlexNet

人脸表情会受到姿势、物体遮挡、光照变化以及人种性别年龄等因素的影响,需要卷积神经网络更有效准确地学习特征。AlexNet在表情识别中准确率不高,对输入图像尺寸有限制,针对这些问题,提出了改进AlexNet网络的人脸表情识别算法。在AlexNet网络中引入多尺度卷积更加适用于小尺寸的表情图像,提取出不同尺度的特征信息,并在把多个低层次特征信息在向下传递的同时与高层次特征信息进行跨连接特征融合,从而可以更加完整准确地反映图像信息,构造出更准确的分类器。跨连接会产生参数爆炸,导致网络训练困难,影响识别效果,因此利用全局平均池化对低层次特征信息进行降维,可减少跨连接产生的参数和过拟合现象。本文算法在CK+、JAFFE数据库上的准确率分别为94.25%和93.02%。

Face expressions are affected by factors such as poses,object occlusion,lighting changes,race,gender,and age.Convolutional neural networks are required to learn features more effectively and accurately.AlexNet has low accuracy in expression recognition and strong input image size limitation.In response to these problems,this paper proposes an improved facial expression recognition algorithm for improved AlexNet networks.Introducing multi-scale convolution to the AlexNet network is more suitable for small-scale expression images,extracting feature information of different scales,and cross-connecting feature fusion with higher-level feature information can be realized while the multiple lower-level feature information is transfered downwards,which can reflect the image information more completely and accurately,and construct a more accurate classifier.Because cross-connections will generate parameter expansion,making network training difficult and affecting recognition results.Therefore,we use global average pooling to reduce the dimensionality of low-level feature information,reduce parameters generated by cross-connections,and reduce overfitting.The accuracy of our algorithm on CK+and JAFFE databases is 94.25%and 93.02%,respectively.