融合改进PHOG与KPCA的人脸识别算法

The Face Recognition Algorithm Based on Improved PHOG and KPCA

针对PHOG特征在描述人脸形状时容易受到梯度强度突变及噪声干扰的缺点,提出了一种基于改进PHOG特征的人脸识别算法。首先对仅适用于描述清晰人脸轮廓形状的PHOG特征进行了改进,使其对人脸局部结构描述更加精细化,并通过改进的归一化方法达到对噪声的抑制,最后通过KPCA变换将改进的PHOG特征非线性映射到高维核空间中,进一步选择区分能力较强的特征分量,用最近邻分类器进行分类。在ORL、FERET和YALE人脸库中做了多组实验分别取得了98%、95%及98.67%的识别率,实验证明:该算法在抑制轮廓噪声提高识别率方面达到了较好的效果。

Since the Pyramid Histogram of Oriented Gradients （PHOG） has a poor performance in describing the shape of faces with noise or abrupt intensity changes, a face recognition algorithm based on imProved PHOG is proposed. Firstly, we improve the PHOG feature used in the clear outline face recognition to describe the further refinement of the local structure of the face. Then the noise is restrained through the improved normalized method. Finally, KPCA is used to project improved PHOG feature into the more expressive kernel space to further select the feature with strong descriptive ability and the nearest method is adopted for classification. The experimental results show that the characteristics combined improved PHOG with KPCA has obvious advantages in face recognition, and the experimental results on ORL, FERET and YALE face database can achieve high face recognition rate up to 98%, 95% and 98.67%. It is shown that the proposed method has better effect on noise suppression and improving the recognition rate.