Face detector combining eigenfaces, neural network and bootstrap

A critical issue in an automatic face recognition system is the determination of the region containing a face in an image with a cluttered background. The paper presents a method that optimizes the detection task through the use of eigenfaces, neural networks and a bootstrap algorithm. The main component of the proposed method is a nonlinear operator that detects the presence of a well-framed face image in 20x20 pixel windows. To detect faces larger than the window size the input image is successively reduced by a factor of 1.2 and the procedure is applied at each scale. To obtain a compact representation of the face images, the method applies principal component analysis directly to the pixel intensities of face images. Each image window analyzed by the detection algorithm is then projected upon the n principal components, the so-called eigenfaces. The dimensionality reduction thus achieved implies in a reconstruction error, the DFFS-distance from features space. The patterns representing an image window are formed by the n projections plus the DFFS.