earticle

영어

In view of the greater changes of posture, illumination, expression and scene in reality environment have a strong impact on wild face recognition algorithm to identify performance problem, and puts forward a kind of linear discriminant analysis side information (SILD) algorithm on hyperplane fusion of learning prototype. First of all, using support vector machine (SVM) to weak tag of data-concentrated sample is expressed as the middle-level characteristics of prototype hyperplane, using a learning combination coefficient to select sparse support vector set from untagged conventional data set; then, under the constraints of the combination sparse coefficient of SVM model, by using Fisher linear discriminant criterion to maximize discriminant ability of untagged data set, and using the iterative optimization algorithm to solve the objective function; in the end, using SILD for feature extraction, cosine similarity measure to complete the final face recognition. In two general face data sets of wild face recognition (LFW) and YouTube, it makes comparison of PHL+SILD method and low-level features + SILD method on some characteristics, such as strength, LBP, Gabor feature and Block Gabor feature, average accuracy, area under the curve (AUC) and entire error rate (EER). The validity and reliability of the proposed algorithm is verified by the experiments.