Face Recognition Based on Bidirectional Gradient Center-Symmetric Local Binary Patterns
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摘要: 针对单样本情况下传统人脸识别方法识别效果不佳的问题，提出一种双向梯度中心对称局部二值模式（BGCSBP）的单样本人脸识别算法.首先获取人脸水平和垂直方向的梯度信息，并将其用CS-LBP算子进行编码；然后将二者融合成人脸的BGCSBP特征，再通过分块统计直方图的方式得到人脸的直方图特征；最后采用直方图相交进行分类识别.在CAS-PEAL，Extend Yale B和AR人脸数据库上的实验结果表明，该算法简单有效，对光照、表情、部分遮挡变化具有较好的鲁棒性.
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关键词:
- 人脸识别 /
- 单样本 /
- 双向梯度 /
- 中心对称局部二值模式 /
- 直方图相交
Abstract: To overcome the limitations of traditional face recognition methods for single sample, a novel method of face recognition based on bidirectional gradient center-symmetric local binary pattern(BGCSBP) is proposed. Firstly horizontal gradient and vertical gradient of face image are calculated, and center-symmetric local binary pattern(CS-LBP) is proposed to encode the gradient. Secondly the proposed BGCSBP is the combination of the CS-LBP of horizontal gradient and vertical gradient. BGCSBP feature maps are divided into several blocks and the concatenated histogram features calculated over all blocks are used for the feature descriptor of face recognition, and the recognition is performed by using the histogram cross. This experimental results on CAS-PEAL, Extend Yale B and AR face databases show that the algorithm is simple and effective, and robust to variations of face illumination, face expression and partial occlusion conditions. -
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