Face detection using neural learning with optimized feature set
A system using feature optimization and neural learning techniques is presented for detection of upright fronta l faces. The design of the system takes advantages of the existing techniques and avoid their shortcomings. Statistic al pattern recognition (PR) techniques are used to optimize th e feature selection. A neural network (NN) method is used to learn a complex mapping function for the classification, given the optimized feature set. The optimization of feature set reduces the burden of the subsequent NN classifier and improves its performance in learning speed and classification rates. The use of the NN for classification avoids the need for the simplification of classifier function, as pra cticed in the PR approach, for the mathematical tractability at the sacrifice of the performance. Experimental results show that our system produces higher detection and lower missing rates than several existing state-of-the-art face dete ction systems, with an average false detection rate.