Face Recognition Research Based On Spline Dyadic Wavelet

Face recognition is a biometrie identification technology with best development potential, and researching on face recognition technology has great theoretical and practical values. After more than forty years of research, considerable progress has been made on the problems of face recognition, especially under stable conditions such as small variations in lighting, facial expression and poses. All face recognition algorithms, however, witness a performance drop whenever face appearances are subject to variations by factors such as occlusion, illumination, expression, pose, accessories and aging. So, it's still a long way to reach a satisfying performance.In this paper, the face recognition algorithms based on wavelet transform are discussed and analyzed. On this basis, a new face recognition approach using spline dyadic wavelet transform is proposed and studied.The principal research work and novelties are listed as follows:1.Provide a detailed survey of face recognition based on wavelet transform.Wavelet transform can be classified into three types, namely continuous wavelet transform, discrete wavelet transform and dyadic wavelet transform. In the last decade, a lot of face recognition algorithms based on wavelet transform have been developed. However, based on the difference of wavelet type used, these face recognition algorithms based on wavelet transform can be classified into two major categories.(1)Face recognition method based on discrete orthogonal or biorthogonal wavelet transform, called Discrete Wavelet Method(DWM) for short.(2)Face recognition method based on continuous Gabor wavelet transform, called Gabor Wavelet Method(GWM) for short. Then, their characteristics of two types of methods are analyzed and summarized from the viewpoint of wavelet application in algorithms. Wavelet application can play the role of dimension reduction and denoising smooth in DWM. However, orthogonal or biorthogonal wavelets are not good at extracting facial edge detail features, which results in the fact that the recognition rate of these algorithms in DWM need to be improved further. On the other hand, Gabor wavelets have good ability to extract facial texture features, which can usually bring good recognition performance. However, it is known to us that GWM have high computation complexity.2. Research and prove jointly the feasibility and effectivity of Mallat decomposition algorithm based on dyadic wavelet.At first, two-dimensional stationary dyadic wavelet transform (2D-SDWT) is introduced, it is defined by approximation coefficients, detail coefficients in horizontal, vertical and diagonal directions, which is essentially the extension of two-dimensional stationary wavelet for orthogonal or biorthogonal wavelet filters. Then, the fast algorithm of 2D-SDWT is given. Next,ε-decimated dyadic discrete wavelet transform(ε-DDDWT) and its relation with 2D-SDWT is given, whereεis a sequence of 0's and 1'. At last, Mallat decomposition algorithm based on dyadic wavelet is proposed as a special case ofε-DDDWT. The experimental comparison with Mallat decomposition algorithm based on orthogonal or biorthogonal wavelet shows that Mallat decomposition algorithm based on dyadic wavelet has better edge detection effects. The property will further extend the application field of dyadic wavelet. It has great potential in the fields of pattern recognition, image processing and so on. It is also theoretical basis of all algorithms proposed in the subsequent several sections.3. Research the application of spline dyadic wavelet in area of face recognition in detail.present a new type of face recognition method based on spline dyadic wavelet, which is called Spline Dyadic Wavelet Method (SDWM).Dyadic wavelet in SDWM can not only play the role of dimension reduction and denoising smooth, but also extract good facial edge detail features. Next, a concrete face recognition algorithm SDWT-FFT-PCA are introduced, and then take the algorithm for example, analyze application features of spline dyadic wavelet in SDWM. At last, several meaningful conclusions according to some experiments can be given as follows:(1)Detail subbands in SDWM include a lot of feature information, which is revealed by the fact that the algorithm based on single detail subband has also high recognition rate. By contrast, detail subbands in DWM have much less feature information.(2)Different detail subbands in SDWM are complementary each other for face recognition, so the recognition accuracy can be improved greatly after the subbands are fused by an effective fusion strategy. In comparison, it is more difficult for DWM to improve recognition rate by making use of the complementarity of different detail subbands.(3)In many spline dyadic wavelets, the orthogonal spline dyadic wavelet and a nonorthogonal spline dyadic wavelet have the best performance.4.Propose a face recognition algorithm based on multidirectional facial detail features and two-dimensional linear discriminant analysis.A multidirectional detail subbands extraction method based on spline dyadic wavelet is proposed. The method can produce three detail subbands in 45 degree,135 degree and cross direction by a series of operators such as rotating original image 45 degree,dyadic wavelet transform,rotating image 45 degree in negative direction,crop operation and so on. Then , a face recognition algorithm RSDWT-FFT-2DLDA based on multidirectional facial detail features and two-dimensional linear discriminant analysis (2DLDA) is proposed. The experimental results shows that detail subbands extraction method and RSDWT-FFT-2DLDA are effective.5.Propose two face recognition algorithms based on spline dyadic wavelet and fusion of multi-classifiers.Face recognition algorithms based on spline dyadic wavelet and fusion of multi-classifiers are studied. Aiming at facial expression recognition and face recognition under the condition of illumination perturbations, two concrete algorithms are proposed respectively.(1)facial expression recognition is researched, a face recognition algorithm based on multidirectional facial edge detail features and fusion of multi-classifiers is proposed. The algorithm reach 100% face recognition rate and 85.34% expression recognition rate in JAFFE.(2)face recognition under the condition of illumination perturbations are studied. A face recognition algorithm combining illumination compensation with fusion of multi-classifiers is proposed. The algorithm combines discrete cosine transform (DCT) in the logarithm domain, spline dyadic wavelet, 2DLDA, fusion of multi-classifiers respectively. The proposed algorithm are tested on CAS-PEAL and YaleB face databases, and achieve 83.91% and 100% recognition rate respectively.