Research And Implementation Of Face Recognition System

Recently, with the rapid development of networks and computer technologies, people are expecting to obtain more security and concealment about information. So, the demand of body degree recognition authentication is becoming more and more abroad. Face recognition is one of the primariest identity authentication based on biology character recognition. And it's one of the simplest and the most convenience methods in biology character recognition. For this, face recognition is getting more and more attention and research.Broadly speaking, face recognition includes face detection, face representation and recognition. This paper discusses the related work from the starting face detection and recognition. First, according to the characteristics of face images, discuss the face image preprocessing and the knowledge of the feature extraction. Details are as follows: grayscale face images, histogram and equalization, edge detection and wavelet decomposition. Then, describe the basic principles of the Adaboost face detection algorithm and the Hidden Markov Model-based recognition algorithm. This paper proposes an enhanced Adaboost face detection algorithm, and compares it with Viola-Jones'algorithm. Experimental results show that the performance of the proposed algorithm is better and has a good detection rate and recognition rate.Finally, combine with the enhanced Adaboost face detection algorithm and the HMM face recognition algorithm, design and implementate a face recognition software system. Combining with the existing experimental face database and the new face image acquisition, simulate the system. Experiment results show that the design of the face recognition system is stable, and it also has good recognition rates.