提出一种基于自适应加权Curvelet梯度方向直方图(AWCHOG)的人脸识别算法。首先,人脸图像通过基于Wrapping的离散Curvelet变换得到多尺度多方向的Curvelet变换系数;然后按照编码方式将同一尺度下不同方向的特征进行编码融合,获得融合后的幅值域图谱,并通过HOG算子结合分块的方法获得Curvelet变换后融合图像的直方图特征,分别根据每个尺度对人脸识别率的贡献进行计算,得出各尺度的权重;最后融合权重系数以及各尺度的HOG特征,利用最近邻分类器进行分类。通过在ORL、AR和CAS-PEAL三个人脸库的实验可以看出,所提算法在人脸图像部分遮挡、姿态、表情、光照变化以及噪声等因素干扰下具有较好的识别效果。

Herein, a face recognition algorithm based on an adaptive weighted Curvelet gradient direction histogram is proposed. First, the Curvelet transform based Wrapping is used to extract facial features with multi-orientations, and the coding method is exploited to fuse the original Curvelet features that have the same scale. Second, the fused image is divided into numerous equal-sized non-overlapping rectangular blocks. The face image is then described using the histogram sequence extracted from all the blocks using the HOG operator, and the adaptive weighting of histograms with each scale is separately performed. Finally, the extracted features are fed into the nearest neighbor-based classifier. Results of the simulation experiments conducted using the ORL, YALE, and CAS-PEAL face databases show that the proposed algorithm has a high face recognition rate and good robustness under the influence of interference factors such as face occlusion, gesture transformation, expression transformation, and illumination transformation.