基于HOG-CSLBP与深度学习的跨年龄人脸识别算法

针对人脸识别中识别精度低的问题,提出一种基于深度学习的跨年龄人脸识别算法.该方法创新性地将方向梯度直方图(Histogram of Oriented Gradient,HOG)和中心对称局部二值模式(Center Symmetric Local Binary Pattern,CSLBPS)组合方法用于人脸图像特征提取,获得包含结构和强度信息的图像融合特征,然后使用二叉树对特征信息进行降维,降维特征作为深度信念网络的可视层输入量,弥补深度新信念网络无法达到图像局部特征要求的缺陷.通过训练好的深度网络模型对测试样本进行学习,在深度信念网络的最顶层对特征进行分类识别.实验结果表明,该方法能高精度实现人脸识别,且与其他方法比较,该方法性能优于其他方法,说明该方法具有可行性和有效性.

Cross-Age Face Recognition Algorithm Based on HOG-CSLBP and Deep Learning

Aiming at the problem of low recognition accuracy in face recognition, a cross-age face recognition algorithm based on deep learning has been proposed. In this method innovatively, the combination of Histogram of Oriented Gradient (HOG) and Center Symmetric Local Binary Pattern (CSLBPS) has been conducted for face image feature extraction and the image fusion features been obtained including the structure and intensity information, and then the binary tree been used to feature the features. The dimensionality reduction feature has been used as the visual layer input of the deep belief network, which makes up for the defect that the depth of the new belief network cannot meet the local features of the image. The test samples have been learned through the trained deep network model, and the features been classified and identified at the top level of the deep belief network. The experimental results show that the proposed method can realize face recognition with high precision, and compared with other methods, the performance of this method is better than other methods, which shows the feasibility and effectiveness of the proposed method.