自适应加权局部相位量化的人脸识别

Face Recognition Based on Adaptively Weighted LPQ

针对局部相位量化(LPQ)方法描述图像特征时不能对各个子图像不同的贡献率加以区分的问题,提出了一种自适应加权局部相位量化(AWLPQ)的人脸识别方法。首先对人脸图像进行分块并在每个子图像上进行LPQ特征提取,然后将信息熵作为衡量各个子图像对整体人脸描述的贡献度的依据,对每个子图像进行自适应加权。在FERET数据库上进行的实验表明AWLPQ具有较好的识别性能。随后针对AWLPQ中存在的高维向量问题,作了进一步分析,引入了近邻保持嵌入(NPE)的流形算法进行降维,提出了AWLPQ-NPE方法。实验结果表明该方法具有很好的鲁棒性和识别性能。

In order to address the problem that Local Phase Quantization （LPQ） method couldn＇t discriminate among the sub-patterns based on their different contribution when describing the image feature. A method for face recognition called as Adaptively Weighted Local Phase Quantization （AWLPQ） is proposed. At first, the face images are divided into several sub-images and the feature fetch is based on the LPQ method. And then proposed algorithm employs an adaptively weighting map to weight the sub-patterns based on their information entropy which is defined as the contribution to describe the whole face images. Experiments on the FERET face database show that the proposed method is effective. In addition, in order to solve the problem of high dimension in AWLPQ, Neighbor Preserving Embedding （NPE） is applied for dimension reduction. The experimental results indicate that the method gains both relative robustness and good recognition accuracy.