Frontal face detection using support vector machines and back-propagation neural networks

Face detection is a key problem in building systems that perform face recognition/verification and model-based image coding. Two algorithms for face detection that employ either support vector machines or backpropagation feedforward neural networks are described, and their performance is tested on the same frontal face database using the false acceptance and false rejection rates as quantitative figures of merit. The aforementioned algorithms can replace the explicitly-defined knowledge for facial regions and facial features in mosaic-based face detection algorithms