卷积神经网络已成功应用于人脸识别,但是其提取的人脸特征忽略了局部特征。为了提取更加全面的人脸特征,提出一种将人脸特征融合与卷积神经网络结合进行人脸识别的算法。该方法将人脸图像经离散余弦变换后所获得的低频系数和人脸图像的局部二值模式特征分别作为人脸的全局特征和局部特征,再将两者加权融合后得到的图像输入卷积神经网络进行训练分类。在ORL和CAS-PEAL人脸数据库进行实验和数据分析,结果表明,该方法可以明显地提升人脸识别精度。

Convolutional neural network has been successfully applied to face recognition, but the extracted features ignore the local features of the face. In order to extract more comprehensive facial features, a convolutional neural network based on feature fusion for face recognition is proposed. This method takes the low frequency coefficients of the face images obtained by performing discrete cosine transform as global feature of the face. Besides, extracting local binary pattern features of original face images as local features of the face. Likewise, the image obtained by weighted fusion of global and local features is fed into the convolutional neural network for training. Experimental results in ORL and CAS-PEAL database show that the proposed method can improve the accuracy of face recognition.