Research On Face Recognition Methods Based On Subspace

Face recognition is a hot topic in pattern recognition, the key of facerecognition is feature extraction.The subspace methods have been the mostpopular owing to their calculation convenient and good performance onexpression. The idea of the linear subspace analysis methods are based oncertain performance goals to find a linear space transformation which canreduce a high-dimensional original sample space to a low-dimensional spacethat is benefit to classfication. The distribution of the data is more compact inthe subspace.This dissertation focuses on the robustness of face recognitionbased on linear subspace methods.This dissertation studies the typical linear subspace analysis methods. Toachieve integral projection for pretreatment and PCA method and SPCAmethod for face recognition.Analysis the advantages and disadvantages of themethod as well as the training set and the characteristic dimension impact onthe recognition rate. The PCA method is based on the principle of minimummean square error, which is no distinction the sample of within-class andbetween-class.The SPCA method validation even component is moreimportant for face recognition, while the odd component is vulnerable to theimpact of external conditions.In order to distinguish between-class and within-class sample better, theLDA method is studied. The factors that affect this method including thehigh-dimension small sample, the edge values and discriminant feature spaceorthogonality problem. Research aimed at linear subspace analysis methods, aFisherface algorithm which has two-step dimensionality reduction was beenproposed.In order to improve the orthogonality of discriminant subspace inFisherface and increase the recognition rate, a new algorithm called matrix symmetry of Fisherface was proposed. Firstly, the PCA was used fordimensionality reduction to eliminate the small sample size problem. Secondly,the Fisher criterion was redefined by introducing a symmetric matrix. Finally,some examples were classified by the symmetric matrix. Experimental resultsindicate that the proposed method is more effective than the previous ones.In order to improve the recognition rate of discriminant analysis algorithm,a new algorithm called outlier value substitution of Fisherface was proposed.Firstly, the PCA was used for dimensionality. The substitution or correction ofsample is accorded to the distance between the feature vector and others in thesame class. Secondly, the new class means are calculated using new samples.Finally, the within-class scatter matrix and between-class scatter matrix arerebuilt. Experimental results on ORL face image database indicate that theproposed method is more effective than the previous one and adaptivelyweighted Fisherface.Studied another method to solve the problem of high-dimension and smallsample size, which is to directly solve the Fisher criterion. Studied andimplemented the two projection methods and discusses the impact of weightedvalue.