基于正交梯度二值模式的单样本人脸识别

Single sample face recognition based on orthogonal gradient binary pattern

针对传统人脸识别方法在单样本条件下识别效果不佳的问题,提出一种改进的对光照和表情姿态等变化具有较强鲁棒性的梯度脸算法——正交梯度二值模式(OGBP)。首先采用正交梯度二值模式对样本图像进行特征提取,然后将每个方向特征向量串接起来作为用于人脸识别的总体特征向量,最后通过主成分分析(PCA)方法降维并利用最近邻分类器分类识别。在YALE和AR人脸库上进行测试,实验结果表明所提方法简单有效,性能优于原始的梯度脸算法,且对单样本人脸描述具有更好的效果。

To overcome the limitations of traditional face recognition methods for single sample, an improved gradient face algorithm named Orthogonal Gradient Binary Pattern （OGBP）, which was robust to variations of illumination, face expression and posture, was proposed. Firstly, the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis （PCA） method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. The experimental results on YALE and AR face database indicate that the proposed method is simple, effective and better than the original gradient face algorithm, and also has better performance in face description for single sample.