Human Face Recognition Matlab Program 1 for face recognition Introduction Biometrics_and_ human_biometrics Performance_of_ biometric_system Correlation_pattern_recognition_for_face_recognition Overview_of_correlation_filters Face_recognition_with_correlation_filters Graceful_degradation Shift_invariance Phase_only_correlation 1d_phase_only_correlation Face_recognition_methods 3_d_face_recognition Achieving_illumination_invariance Method_details Adaptive_framework Principal_component_analysis Linear_discriminant_analysis Neural_networks Feedforward_neural_networks Learning_algorithm Training_and_testing_of_neural_networks 3d_face_recognition Challenges_for_face_recognition From_2d_to_3d_face_recognition Advantages_and_disadvantages_of_3D_face_recognition 3d_face_matching Matlab_program_1_for_face_recognition Matlab_program_2_for_face_recognition Matlab_program_3_for_edge_detection Matlab_program_4_noise_insertion Fourier_transform_of_2_diff Edge_detection_cross_correlelation Utility_of_sobel_and_canny_for_edge_detection Human_face_detection_conclusion MATLAB Programs for Face recognition Program 1. I = imread('cara_03.jpg'); J= imread('cara_15.jpg'); %J = imnoise(I,'salt & pepper',0.02); BW1 = im2bw(I,.8); BW2= im2bw(J,.23); imshow(BW1);figure;                                        imshow(BW2);figure; %cov(count(:,1)) p=corr2(BW1,BW2); %p=BW1 * BW2; [X,Y]=meshgrid(-1:.005:1); Z=peaks(p); %imshow(p);figure; %plot(Z,p); mesh(X,Y,Z); Z = fspecial('gaussian'); freqz2(Z)                                Figure 1                                                     Figure 2     Figure3                         First of all cara_11.jpg & cara_15.jpg two colour pictures are read into I & J respectively. Then im2bw converts these two colour pictures into black & white pictures and they are saved in BW1 & BW2 respectively.These two pictures are shown in figure1 & figure2.BW1 & BW2 are two different matrices. So correlation is done in between these two matrices by using “corr2” and the result is saved in “p” which is passed as the parameter in peaks() function to find out the peak of the correlation result.If this peak is sharp then the two pictures are almost same but if the peak is not so sharp then we can conclude that there is difference in between those two pictures. As in the above case peak is not so sharp, so we can conclude that the two faces are of two different human being. Want To Know more with Video ??? Watch the latest videos on YouTube.com Contact for more learning: webmaster@freehost7com             `The contents of this webpage are copyrighted © 2008 www.freehost7.com` ` All Rights Reserved.`