Face detection using neural learning with optimized feature set
Shenyu Li · 2000
A system using feature optimization and neural learning techniques is presented for detection of upright frontal faces. The design of the system takes advantages of the existing techniques and avoid their shortcomings. Statistical pattern recognition (PR) techniques are used to optimize the 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 practiced 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 detection systems, with an average false detection rate.