关键词: 鲁棒人脸识别, 光照变化, 局部线性嵌入, 光谱回归, 流形学习

Abstract: For the robust face recognition problem with high-dimensional small sample, the algorithm of spectral regression classification optimized by local linear embedding is proposed. Firstly, feature vectors of training samples are calculated. Then, local linear embedding is used to construct embedding needed by classification and embeddings needed by sub-manifold of each classification is learned. Finally, spectral regression classification algorithm is used to compute project metrics, and nearest neighbor classifier is used to recognize face. Experimental results on the common face datasets FERET, AR and Extended YaleB show that proposed algorithm has better recognition efficiency than several other spectral regression algorithms.

Key words: robust face recognition, illumination variation, local linear embedding, spectral regression, manifold learning