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    <p>I believe you can also try searching for "Face Verification" rather than just "Face Recognition". This might give you more relevant results.</p> <p>Strictly speaking, the 2 are actually different things in scientific literature but are sometimes lumped under face recognition. For details on their differences and some sample code, take a look here: <a href="http://www.idiap.ch/~marcel/labs/faceverif.php" rel="noreferrer">http://www.idiap.ch/~marcel/labs/faceverif.php</a></p> <p>However, for your purposes, what others such as Edvard and Ari has kindly suggested would work too. Basically they are suggesting a K-nearest neighbor style face recognition classifier. As a start, you can probably try that. First, compute a feature vector for each of your face images in your database. One possible feature to use is the Local Binary Pattern (LBP). You can find the code by googling it. Do the same for your query image. Now, loop through all the feature vectors and compare them to that of your query image using euclidean distance and return the K nearest ones. </p> <p>While the above method is easy to code, it will generally not be as robust as some of the more sophisticated ones because they generally fail badly when faces are not aligned (known as unconstrained pose. Search for "Labelled Faces in the Wild" to see the results for state of the art for this problem.) or taken under different environmental conditions. But if the faces in your database are aligned and taken under similar conditions as you mentioned, then it might just work. If they are not aligned, you can use the face key points, which you mentioned you are able to compute, to align the faces. In general, comparing faces which are not aligned is a very difficult problem in computer vision and is still a very active area of research. But, if you only consider faces that look alike and in the same pose to be similar (i.e. similar in pose as well as looks) then this shouldn't be a problem.</p> <p>The website your gave have links to the code for Eigenfaces and Fisherfaces. These are essentially 2 methods for computing feature vectors for your face images. Faces are identified by doing a K nearest neighbor search for faces in the database with feature vectors (computed using PCA and LDA respectively) closest to that of the query image.</p> <p>I should probably also mention that in the Fisherfaces method, you will need to have "labels" for the faces in your database to identify the faces. This is because Linear Discriminant Analysis (LDA), the classification method used in Fisherfaces, needs this information to compute a projection matrix that will project feature vectors for similar faces close together and dissimilar ones far apart. Comparison is then performed on these projected vectors. Here lies the difference between Face Recognition and Face Verification: for recognition, you need to have "labels" your training images in your database i.e. you need to identify them. For verification, you are only trying to tell whether any 2 given faces are of the same person. Often, you don't need the "labelled" data in the traditional sense (although some methods might make use of auxiliary training data to help in the face verification).</p> <p>The code for computing Eigenfaces and Fisherfaces are available in OpenCV in case you use it.</p> <p>As a side note: A feature vector is actually just a vector in your linear algebra sense. It is simply n numbers packed together. The word "feature" refers to something like a "statistic" i.e. a feature vector is a vector containing statistics that characterizes the object it represents. For e.g., for the task of face recognition, the simplest feature vector would be the intensity values of the grayscale image of the face. In that case, I just reshape the 2D array of numbers into a n rows by 1 column vector, each entry containing the value of one pixel. The pixel value here is the "feature", and the n x 1 vector of pixel values is the feature vector. In the LBP case, roughly speaking, it computes a histogram at small patches of pixels in the image and joins these histograms together into one histogram, which is then used as the feature vector. So the Local Binary Pattern is the statistic and the histograms joined together is the feature vector. Together they described the "texture" and facial patterns of your face.</p> <p>Hope this helps.</p>
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    1. POAlgorithms for finding a look alike face?
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    1. COCan you explain a bit more a bout feature vector? I'm reading about vector compression and there too you need to pick a feature vector of the color.
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    2. CO@phpdevpad I added a paragraph to clarify the part on feature vectors. I think for you case, the feature vector could be just the 3 x 1 vector containing the RGB values, 1 for each pixel in the image. In vector compression/quantization, you cluster these feature vectors to form a codebook/dictionary of K clusters (usually using K Means), then replace each pixel with the cluster center of the cluster it is closest to. Hope this helps.
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    3. COI'm sorry it was about antipole clustering. I gave this answer to somebody: http://stackoverflow.com/questions/13288571/antipole-clustering because in antipole clustering you pick a feature vector of 27 rgb values. Does this mean you just sum them up? In your word n x 27?? In vector compression I wrote a vector f(x,y) is the current pixel value c(x,y) - the adjacent pixel r(x1,y1) to the right and the adjacent pixel at the bottom b(x2,y2)?
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