基于卷积特征和贝叶斯分类器的人脸识别

为解决传统人脸识别算法特征提取困难的问题，提出了基于卷积特征和贝叶斯分类器的人脸识别方法，利用卷积神经网络提取人脸特征，通过主成分分析法对特征降维，最后利用贝叶斯分类器进行判别分类，在ORL（olivetti research laboratory）人脸库上进行实验，获得了99.00%的识别准确率。实验结果表明，卷积神经网络提取的人脸图像特征具有很强的辨识度，与PCA（principal component analysis）和贝叶斯分类器结合之后可有效提高人脸识别的准确率。

Face recognition based on convolution feature and Bayes classifier

To solve the difficulty of feature extraction of the traditional face recognition algorithm, a new method based on convolution feature and Bayes classifier is proposed, which uses convolution neural network to extract facial features and principal component analysis (PCA) to reduce the feature dimension, and finally, employs a Bayes classifier to classify the features. Experiments were carried out on the ORL face database, and a recognition accuracy of 99% was achieved. The experimental results show that the face features extracted by the convolution neural network have a strong degree of recognition. Therefore, the accuracy of face recognition in feature extraction can be effectively improved by combining PCA and Bayes classifier with convolution neural network.