Study Of Face Recognition Based On Wavelet Packet And Wavelet Multi-level Linear Subspace Feature Extraction Algorithm

In many biometric identification technology, because its unique initiative, non-invasive and user-friendly advantages, face recognition technology has been widespread concern in academia and the business community. Face recognition has been widely used in the regional counter-terrorism, military security, the financial sector, e-commerce, e-government as well as personalized service a number of occasions. So face recognition has a huge potential market applications background and high academic research value.One of the key issues of the face recognition is feature extraction. On the basis of extensive literature research, in-depth study feature extraction algorithm of the linear subspace. Including four algorithms:The principal component analysis (PCA), two-dimensional principal component analysis (2DPCA), two-dimensional linear discriminant analysis (2DLDA), and the Fisherface (PCA+LDA). The comparative study of recognition accuracy and run-time of four feature extraction algorithms in the ORL face database and YALE face database. That is:1. Expound four feature extraction algorithms of the principle of PCA,2DPCA,2DLDA and PCA+LDA.2. Testing the changes of the recognition accuracy in the ORL and YALE face database in the four algorithms based on different number of training samples.3. Testing the changes of the run-time in the ORL and YALE face database in the four algorithms based on different number of training samples.Meanwhile, in order to effectively improve the recognition rate, the paper further proposed a wavelet packet and wavelet multi-level thinking:make the image to wavelet packet decomposition in the first layer. And then make layer of the first layer of the wavelet packet decomposition node (1,0) to wavelet decomposition in the first layer. The idea is applied to the four extraction algorithm and achieved good results in terms of recognition rate. These include:1. Propose four algorithms of PCA of wavelet packet and wavelet multi-level,2DPCA of wavelet packet and wavelet multi-level,2DLDA of wavelet packet and wavelet multi-level and PCA+LDA of wavelet packet and wavelet multi-level.2. Tested the changes of the accuracy of the four algorithms based on different wavelet bases respective in the ORL and YALE face database.3. Tested the changes of run-time of the first three algorithms based on different wavelet bases respective in the ORL and YALE face database. The results of the experiment shows:Recognition rate of four algorithms was increased after joining the wavelet, and the recognition rate of2DLDA algorithm of wavelet packet and wavelet multi-level is99.5%in ORL database.In short, the thousands of different parameters of experimental study have been done in the paper, and obtain a high recognition rate and satisfactory identification. Experiments and validation the PCA,2DPCA,2DLDA, PCA+LDA, PCA of Wavelet packet and wavelet multi-level.2DPCA of wavelet packet and wavelet multi-level,2DLDA of wavelet packet and wavelet multi-level and PCA+LDA of wavelet packet and wavelet multi-level in different parameters, different training samples, different wavelets, ORL and YALE face database role and effect, and obtain comparative characteristics and results. The research work of this paper provides a rich, detailed experimental data and the experimental curve in further research, has guide and reference for the study of face recognition.