Gabor字典及l0范数快速稀疏表示的人脸识别算法

针对光照、遮挡、伪装情况下，识别率比较低，识别时间长的问题，本文提出了基于Gabor字典及l0范数快速稀疏表示的人脸识别算法。Gabor小波提取的特征能够克服遮挡、光照等干扰对人脸识别的影响，平滑l0算法通过平滑连续函数来近似 l0范数，只需较少测量值并且较快速度便能重构稀疏信号。本算法通过提取人脸的Gabor特征、主成分分析法（PCA）降低维度，l0范数快速稀疏分类完成识别。在伪装人脸情况下，分块计算Gabor人脸特征，提高Gabor字典的形成速度。基于AR人脸数据库的实验结果表明，本算法可在一定程度上提高识别速度和识别时间，即使在小样本情况下，依然具有较高的识别率。 

Face recognition based on fast sparse representation of Gabor dictionary and l0 norm

Many classic face recognition algorithms degrade sharply when they are used at identifying an individual under various conditions such as illumination, camouflage. A fast sparse representation face recognition algorithm based on Gabor dictionary and smoothed l0 norm is proposed in this paper. The Gabor filters, which could effectively extract the image local directional features at multiple scales, are less sensitive to the variations of illumination and camouflage. Smoothed l₀ algorithm requires fewer measurement values by continuously differentiable function approximation? l₀ norm. The algorithm obtains the local feature by extracting the Gabor feature, reduces the dimensions by principal component analysis (PCA) and realizes fast sparse by the? l₀ norm. Under camouflage condition, the algorithm blocks the Gabor facial feature and improves the speed of formation of the Gabor dictionary. Experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate and can generalize well to the face recognition, even with a few training image per class.