face-recognition

I'm trying to implement a face recognition algorithm using Python. I want to be able to receive a directory of images, and compute pair-wise distances between them, when short distances should hopefully correspond to the images belonging to the same person. The ultimate goal is to cluster images and perform some basic face identification tasks (unsupervised learning). Because of the unsupervised setting, my approach to the problem is to calculate a "face signature" (a vector in R^d for some int d) and then figure out a metric in which two faces belonging to the same person will indeed have a

In my application im going to implement face Recognition login... so i go with the openCV library for Recognize face... please help me to do this with sample code and tutorials.... Thanks in advance a.bertucci Well, my colleagues and I did some investigation on face recognition last year, and these are some of ours considerations about using integrated recognition tools vs JavaCV (the Java bindings for OpenCV): Please check below tutorials Face Detection on Andriod Part-I ( Wayback link ) Face Detection on Andriod Part-II ( Wayback link ) Hope it helps :) you can use NDK for using C/C++ OpenCV

I have referred the documentation provided by OpenCV to make a face recognition program, it recognizes multiple faces and is working normally. In the documentation they have made ellipses to highlight the face. What I don't understand is how they have calculated the center of the ellipse which they have calculated as follows for( int i = 0; i < faces.size(); i++ ) { Point center(faces[i].x+faces[i].width*0.5,faces[i].y+faces[i].height*0.5); //more code follows drawing the ellipse The faces vector that they are using is produced as follows face_cascade.detectMultiScale(frame_gray,faces,1.1,2,0

I am trying to implement a basic face recognition system using Uniform Circular LBP (8 Points in 1 unit radius neighborhood). I am taking an image, re-sizing it to 200 x 200 pixels and then splitting the image in 8x8 little images . I then compute the histogram for each little image and get a list of histograms . To compare 2 images , I compute chi-squared distance between the corresponding histograms and generate a score. Here's my Uniform LBP implementation: import numpy as np import math uniform = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 58, 6: 5, 7: 6, 8: 7, 9: 58, 10: 58, 11: 58, 12: 8, 13: 58,