基于堆栈降噪自编码器和LBP的人脸识别算法
Face Recognition Algorithm Based on Stack Denoising Autoencoders and LBP
DOI: 10.12677/CSA.2018.86096, PDF,  被引量    科研立项经费支持
作者: 刘晓敏*, 艾震鹏：广东工业大学应用数学学院，广东 广州
关键词: 人脸识别；局部二值模式；堆栈式降噪自编码器；支持向量机；Face Recognition； Local Binary Patter； Stack Denoising Autoencoders； Support Vector Machine
摘要: 针对传统的人脸识别算法的鲁棒性弱，分类准确率不高，运算速率较慢的缺点，基于局部二值模式(Local Binary Pattern)和堆栈式降噪自编码器(Stack Denoising Autoencoders)模型，提出了一种LBP + SDAE新的人脸表情识别算法。首先，先对图像进行直方图均衡化处理，用LBP提取特征，接着进行尺度归一化处理后用SDAE二次提取特征并且去噪，降维，最后用SVM分类。该方法不仅提高了分类的准确率，而且加快了运算的速率。在数据集Yale上进行验证，表明相对于以前传统的人脸识别算法，它具有更高的准确率和较强的鲁棒性。
Abstract: In view of the weak robustness of the traditional face recognition algorithm, the low accuracy of classification and the slow operation rate, this paper proposes a new LBP + SDAE facial expression recognition algorithm, which is based on Local Binary Pattern and Stack Denoising Autoencoders. First, the image is processed by histogram equalization and the feature is extracted with LBP. Then, the scale normalization is followed by using SDA to extract the feature again, to reduce image denoising and dimensionality. Finally, the SVM algorithm was selected as classifier for the recognition of images. This method not only improves the accuracy of classification, but also accelerates the computation speed. The YALE face database was used to test the proposed method. The experiment results show that it has higher accuracy and robustness compared with the traditional face recognition algorithm.
文章引用：刘晓敏, 艾震鹏. 基于堆栈降噪自编码器和LBP的人脸识别算法[J]. 计算机科学与应用, 2018, 8(6): 867-876. https://doi.org/10.12677/CSA.2018.86096

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