关键词: 户外人脸识别, 中层特征表示, 支持向量机, 奇异值分解, 线性判别边信息

Abstract: In unconstrained condition, illumination, posture and facial expression becomes the main choke point of wild face recognition. Based on this issue, a fusion algorithm is proposed with learning prototype hyperplanes and side-Information based linear discriminant analysis is proposed. Support Vector Machine（SVM） model is used to each sample in weak labeled data set to be mid-level feature of prototype hyperplanes, and SVM sparse set is selected from generic data set without labeled by learning combination coefficient. Fisher discriminative criterion is used to maximize discriminat ability under the constraint of combination sparse coefficient of SVM model, and the objective function is solved by iterative optimization algorithm. SILD is used to extract features and cosine similarity measure is used to finish face recognition. The effectiveness and reliability of proposed algorithm has been verified by experiments on the two common databases Extended YaleB and labeled face of wild（LFW）. The results show that proposed algorithm has better recognition efficiency comparing with several other face recognition algorithms.

Key words: wild face recognition, mid-level feature representation, Support Vector Machine, Singular Value Decomposition, side-information based on linear discriminant