针对目前图像特征点人脸识别算法匹配精度低等缺点，提出了一种基于卷积神经网络的人脸识别算法。该算法采用传统算法算子融合卷积神经网络进行识别，首先采用局部感受野的思想，将整体图像进行分割，得到局部图像集合，并将该集合中每个局部图像像素存储在像素矩阵Ai中。然后对各个局部图像进行卷积运算，得到局部图像之间的内在特征联系，存储于Bi矩阵中，并池化进行特征映射。最后，训练出网络加权系数并求出识别结果。实验结果表明，相比其他算法，所提算法改善了原有算法图像特征点匹配精度低的问题，验证了所提算法的有效性。

Aiming at the shortcomings of current image feature point face recognition algorithms such as low matching accuracy, a face recognition algorithm based on convolutional neural network is proposed. The algorithm uses traditional algorithm operators to fuse convolutional neural networks for recognition. First, the idea of local receptive field is used to segment the overall image to obtain a local image set. Each local image pixel in the set is stored in the pixel matrix Ai. Then the convolution operation is performed on each local image to obtain the intrinsic feature relationship between the local images, and it is stored in the Bi matrix and pooled for feature mapping. Finally, the network weighting coefficients are trained and the recognition results are obtained. Experimental results show that compared with other algorithms, the proposed algorithm improves the problem of low matching accuracy of image feature points of the original algorithm, and verifies the effectiveness of the proposed algorithm.