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Face recognition algorithm based on self-adaptive blocking local binary pattern

机译：基于自适应阻塞局部二进制模式的人脸识别算法

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Face recognition is a common means of identity authentication. Mobile learning platform login technology has developed from user name and password to face recognition. In order to improve effectively the rate of face recognition, this paper proposes a kind of face recognition algorithm based on self-adaptive blocking local binary pattern (LBP) and dual channel convolutional neural network (CNN) with different convolution kernels. Firstly, the Gamma correction, the Mallet wavelet filtering and normalization are used to preprocess the face image. The face image is decomposed and reconstructed by 2-layer Mallet wavelet to filter out the interference signal effectively. Although the general LBP operator extracts the overall texture and contour features of the face image, the distribution of the bright spot, dark spot and other micro details cannot be fully characterized. In order to solve this problem, integral projection is introduced to project the image horizontally and vertically. The extreme points of the projection represent the texture mutation points of the face image in the horizontal and vertical directions. These extreme points are used as the boundary of the image blocking, and the LBP value of the face image is extracted by the self-adaptive blocking strategy. Combining the features of k-nearest neighbor classifier and softmax, a k-softmax classification method is proposed to classify and recognize the face image labels. After two channel network structure training, this method is tested on Yale, ORL, extended Yale B and self-built face databases by five experiments, comparing with other face recognition algorithms. The results show that the proposed method based on SAB-LBP and dual channel CNN has high recognition rate and computational efficiency.
机译：人脸识别是身份认证的常见手段。移动学习平台登录技术已从用户名和密码开发以面对识别。为了有效地提高面部识别率，本文提出了一种基于自适应阻塞局部二进制图案（LBP）和双通道卷积神经网络（CNN）的人脸识别算法，其具有不同的卷积核。首先，伽马校正，槌小波滤波和归一化用于预处理面部图像。面部图像被双层槌小波分解并重建，以有效地滤除干扰信号。尽管通用LBP操作员提取面部图像的整体质地和轮廓特征，但亮点，暗点和其他微细节的分布不能完全表征。为了解决这个问题，引入整体投影以水平和垂直地投影图像。投影的极端点代表水平和垂直方向上的面部图像的纹理突变点。这些极端点用作图像阻塞的边界，并且通过自适应阻塞策略提取面部图像的LBP值。组合K-Collect邻分类器和Softmax的特征，提出了一种k-softmax分类方法来分类和识别面部图像标签。经过两个频道网络结构培训，在耶鲁，ORL，延伸的耶鲁B和自制面部数据库上测试该方法，与其他面部识别算法相比。结果表明，基于SAB-LBP和双通道CNN的所提出的方法具有高识别率和计算效率。

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