Research Of Face Feature Extraction And Recognition Bsed On Fast Transform

Face recognition is a widely used biometrics technique,and the face-based biometrics method is more convenient,friendly and covert than biometric methods based on fingerprints,iris and other biometric features,so the face recognition technology The more widely used in people's production and life.In some special occasions,such as public security tracking fugitive identification process,in addition to identify the accuracy rate,the recognition speed is also very important,so the main purpose of this paper is to ensure a certain recognition accuracy at the same time,to speed up face recognition speed.After comparing and analyzing the advantages and disadvantages of traditional face recognition technology,this paper presents a fast Fourier transform and singular value decomposition method combined with face recognition technology.First,the training samples are pre-processed,and then from the airspace to the frequency domain,with the frequency domain amplitude as the face in the frequency domain features that.Secondly,the mean value of the training image after fast Fourier transform and dimensionality reduction is taken as the feature space,and the face features of the row and column are taken as the feature space.Finally,the training samples and test samples are projected into the feature space,using the European distance to do classification and identification.In the whole process,to speed up the identification of the speed is mainly reflected in:First,in the process of switching to the frequency domain,fast Fourier transform in addition to the traditional DCT transform with the energy concentration in order to reduce the dimension of good nature,but also The singular value decomposition can be directly applied to the two-dimensional image matrix,and the principal component analysis method is used to further accelerate the velocity of the spatial region of the face image space.Second,the singular value decomposition is directly applied to the two-dimensional image matrix,and the principal component analysis method Is the initial image to do the stretching,and then calculate the covariance matrix,and then based on the covariance matrix to do orthogonal transformation,so the singular value decomposition matrix size than the main component analysis method is smaller,so when extracting features singular value decomposition in speed Has a clear advantage.The data used in this study are ORL and Yale face database.In the experimental stage,the different recognition parameters of the different methods are used to compare the recognition speed and the recognition accuracy.After the analysis of the experimental results,the face recognition technology based on the fast Fourier transform and the singular value decomposition method ensures a certain recognition accuracy Of the case,the recognition speed has improved significantly,and for the light,noise and face posture transformation and so have a certain degree of robustness.