Abstract: This paper proposes a novel approach for face recognition based on Adaptively Weighted Histograms of Oriented Gradients（AW-HOG）to solve the issues of low face recognition rate in complex environments. Firstly, AW-HOG feature is available by fusing the weighting map and the traditional HOG feature of the sub-images divided from the original whole face images. And the weighting map is adaptively computed on account of the contribution of each sub-image. After that, the dimensions of AW-HOG features are reduced by Principal Component Analysis（PCA）and the final classification features are generated. Finally, Support Vector Machine（SVM）is utilized in face classification and recognition using the final features. Experimental results based on Yale B and AR standard face databases demonstrate that the proposed approach not only obviously enhances face recognition rate in complex environments but also has certain robustness to the influence of light and expression.

Key words: face recognition, Histograms of Oriented Gradients（HOG）, Principal Component Analysis（PCA）, adaptively weighted, Support Vector Machine（SVM）

摘要： 为了提高人脸识别在复杂条件下的识别率，提出一种基于自适应加权梯度方向直方图特征（AW-HOG）的人脸识别方法。该方法首先将人脸图像分成均匀子块，并利用HOG描述算子提取分块人脸特征，根据各分块对识别的贡献率自适应地计算各分块的权重，然后融合权重系数以及各分块的HOG特征，形成AW-HOG特征并采用主成分分析（PCA）算法进行降维，最后利用支持向量机（SVM）进行分类识别。在Yale B 以及AR标准人脸库上的实验结果表明，提出的人脸识别方法在识别率上优于传统算法且对光照具有较强的鲁棒性。

关键词: 人脸识别, 梯度方向直方图, 主成分分析, 自适应加权, 支持向量机