Face recognition application of support vector machines - Evangelista, John (1999)
Face Recognition is inherently difficult. Although we may take it for granted, a computer has map out the face into a higher plane of hundreds of dimensions. This will include the color of the pixels, their positions, etc. A relatively new concept worth applying for a classfication problem is the concept of "Support Vectors". Support Vector Machines is a learning technique developed by V. Vapnik and his team (AT%T Bell Labs). This is a new method for training polynomial, radial basis for functions, and neural network classifiers. The use of Support Vector Machines allows face recognition without feature localization. This means that recognition can be done at lower percentages of error even if face features, like eyes, nose, hairline, etc., are not extracted from a particular face image. The system is divided into two phases. The first phase is for classifying an image as either a face or a non-face class. If it is a face class which means that a face is detected in the image, the dimensions of the image are then reduced minimizing the image into a face. But if it is a non-face class the image is ignored. The second phase is the actual face recognition which uses the output generated by the previous phase.