基于Gabor小波变换多特征向量的人脸识别鲁棒性研究

Research on Gabor Wavelet Transform Feature Recognition Robustness Based on Vector of Face

传统的Gabor小波变换人脸识别技术在曲线奇异性的表达上存在着不足,难以识别包含表情的人脸信息,针对该问题,提出了结合Gabor小波变换和多特征向量的人脸识别算法。算法首先利用Gabor小波变换的频率及方向选择性来提取出人脸的多尺度、多方向上的Gabor特征,并组成联合稀疏模型,通过计算可以得到各个方向和尺度上Gabor特征的共同特征和表情特征,利用这两个特征向量可以精确重构测试图像的特征向量。仿真实验结果表明,所提出的方法能够有效提高带表情人脸图像的正确匹配率,改善识别效果。

There is insufficiency cognition technology that causes in expressing curve singularity for facial expression information hard traditional Gabor wavelet transformation in face re- to identify. This paper proposed a face recognition algorithm combining Gabor wavelet transform and multiple feature vectors. The algorithm firstly utilizes frequency and direction selectivity of Gabor wavelet transformation to extract the Gabor features of face multi-scale and direction and forms a joint sparse model in which the common features and expression characteristics of Gabor can be characterized in all directions and scales via calculation, at the same time, the test image feature vector can be accurately reconstructed using the two feature vector. Finally, the simulation results show that this method can effectively enhance the correct matching ratio of facial expression image and improve the recognition effect.