ARTIFICIAL INTELLIGENCE (AI) SUPPORT VECTOR MACHINES (SVM) APPLIED TO FACE RECOGNITION.

ARTIFICIAL INTELLIGENCE (AI)

SUPPORT VECTOR MACHINES (SVM) APPLIED TO FACE RECOGNITION.

Face recognition has developed into a major research area in pattern recognition and computer vision. Face recognition is different from classical pattern-recognition problems such as character recognition. In classical pattern recognition. there are relatively few classes, and many samples per class. With many samples per class. algorithms can classify samples not previously seen by interpolating among the training samples. On the other hand, in

face recognition, there are many individuals (classes), and only a few images (samples) per person, and algorithms must recognize faces by extrapolating from the training samples.

In numerous applications there can be only one training sample (image) of each person.

In difference space, we are interested in the following two classes: the dissimilarities be

tween images of the same individual, and dissimilarities between images of different people. These two classes are the input to a SVM algorithm. A SVM algorithm generates a decision surface separating the two classes. For face recognition, we reinterpret the decision surface to produce a similarity metric between two facial images. This allows us to construct face-recognition algorithms. The work of Moghaddam et al. [3] uses a Bayesian

method in a difference space, but they do not derive a similarity distance from both positive and negative samples.

Verification is fundamentally a two class problem. A verification algorithm is presented with an image P and a claimed identity. Either the algorithm accepts or rejects the claim. A straightforward method for constructing a classifier for person X, is to feed a SVM algorithm a training set with one class consisting of facial images of person X and the other class consisting of facial images of other people. A SVM algorithm will generated a linear decision surface.

One of the major concerns in practical face recognition applications is the ability of the algorithm to generalize from a training set of faces to faces outside of the training set. We demonstrated the ability of the SVM-based algorithm to generalize by training and testing on separate sets. Future research directions include varying the kernel K, changing the representation space, and expanding the size of the gallery and probe set. There is nothing in our method that is specific to faces, and it should generalize to other biometrics such as fingerprints.