Study Of Face Recognition Algorithms

As the development of the society, automatic verification of identity is required moreand more imperatively in most of the fields. Biometrics is recognized as a reasonable basisfor authentication which is attributed for its self-stability and individual diversity.Therefore, with the metrics of direct, friendly and convenient, auto face recognition madethe most immediate way among all of the verification methods. Automatic face recognitionis a technology which recognizes faces according to face image analysis and featureextraction. There are full of challenges in this study that related to the fields of computervision, pattern recognition, image process, physiology, psychology, cognitive sciences andso on.The present difficulty of the face recognition study is mainly on the poor featureabstraction due to the effect of the light, emotional expression, poses and the like, whichfinally reaches an unwanted results. To deal with problems mentioned above, what themain work have been done are as follows:Firstly, the present popular methods of face recognition are studied, among which,wavelet analysis plays an important role in the signal processing field attributed to thetraits of the multi-resolution analysis. To be treated as one signal, the face can bedecomposed into multi-scales of space and time domains, in which obtains more availableinformation and reaches a better classification result. So, on the base of former researches,the two key problems of wavelet transform including the choices of wavelet anddecomposing scales have been studied here further. Through theoretical analysis andlaboratories testing on face databases, there are some conclusions received such as therelationship between the various characteristics of wavelet function and the manifestationsfor facial feature extraction, the impact of discrimination that has brought out, as well asthe decomposition of different scales for the weakening of the effects on human face bylight, gesture and expression. The research is expected for a useful reference for thechoices of the wavelet function and the decomposing scale in face recognition applications. Secondly, Principal Component Analysis (PCA) has been studied in face recognitionas one of the classic feature extraction algorithm in this fields, which includesone-dimensional and two-dimensional analysis methods. After studying on characteristiclevel fusion, a decision level fusion method has been proposed to improve recognize resultusing information complementary among classifiers. First of all the algorithm, wavelettransform and PCA have been taken to do feature extraction, then, fuzzy classification isdone according to membership approach. Followed by the primitive classifiers, differentweights have been assigned to make a fusion. Results shows that as weights are rightlychosen, it will achieve information complementary and improve the recognize rate.Finally, the algorithm of fuzzy integral fusion applied in face recognition is studied.With the well reflection of the interaction between classifiers, the fuzzy integral hasapplied in many fields and got fine results. Here takes the fuzzy integral to fusion twoclassifiers which classified by the features extracted through wavelet transform and2DPCA. The algorithm is characterized by the integral using fuzzy measure of differentcategories which showed the traits and interactivity to receive the value of all the classes,which chooses the largest integral value as the final result. As one important element in theintegral algorithm, fuzzy measure can be evaluated comprehensively from the therefollowing points as correctness of recognition, describing quality and distinguishabledegree. Experimental results show that the algorithm performs better than a singleclassifier or a formal fusion method using weighs and is applicable in face recognitionsystem.