The Research Of Face Detection And Recognition Based On AdaBoost And ICA Algorithm

Biometric Identification is becoming a recognized identity authentication technology. From the most basic to the most robust, there are different levels of security technology, biometrics will be the safest. The face recognition is the most commonly used in our daily lives means of identity authentication is the most popular research topic in pattern recognition. In generally, automatic face recognition system requires three basic steps to complete:face detection and location, feature selection and extraction, face recognition.In this context, the paper designs and implements a series of face detection and recognition for experimental and research. The following is the main study of this paper.First of all, this paper has studied abroad on the face detection and recognition methods. We compared and summarized these algorithms. Then based on these, we established the direction of this research;Secondly, aimed at the complex and long training process of face detection, this paper address the problem of learning to detect faces from a small set of training samples. Based on Fisher discriminant analysis using linear hyperplane classifier, AdaBoost algorithm constitutes a multi-layer cascaded classifier for face detection. We show that the detection can be significantly improved with our algorithm on a small dataset;Finally, independent component analysis was used to extract the facial features, and the nearest neighbor classifier and support vector machines were both used to identifying, the experiment's result proves the superiority of independent component analysis and support vector machines.