Face Detection And Tracking With Multi-information Fusion Based On Adaboost And Feature Extraction Research

With the most advanced technology ever-changing,face detection and recognition hasbecome a popular research objects in the field of scientific research, face detection as the firststep in face recognition of human-computer interaction, video surveillance, video processing,safety and security and other fields, has broad application prospects. There are many problemsin practical applications, face detection needs to consider lighting, occlusion, tilt, thebackground, face frame detected from the complex changes in the environment and can’t be agood representative of the true position of the face. Due to the special nature of the field, itneeds generally image sequence analysis, real-time algorithm, higher accuracy requirements.Face Recognition related to neural networks and pattern recognition, speech recognition,virtual reality and other fields, biometric technology all-inclusive, even from the beginning ofthe fingerprint recognition, iris recognition, to the face recognition DNA bio informationcontrast identification, face detection and tracking and face recognition are involved in manyareas of research today and occupy a very important position.Firstly, we discuss the face detection algorithm to compare and explore research, takinginto account the AdaBoost algorithm proposed by Paul Viola et al AdaBoost algorithm basedon real-time detection work for the follow-up of the human eye, this paper introduces theCamShift algorithm. CamShift tracking algorithm based on the color model has theadvantages of good real-time, the smaller amount of computation can better track around thetwo face frame rotation, tilt angle information tracking for follow-up eye detection whichprovides priori knowledge. Face detection in the follow-up phase, we introduce the NMIFeature normalization method used for human eye positioning in the face frame, accuratehuman eye positioning lead to increase the accuracy of the face detection. After that, weimprove the positioning of the human eye by introducing upper and lower filter characteristicsof the human eye in the NMI space to raise the recognition rate of the human eye positioning,thereby increasing the accuracy of the face detection.Start with recognition algorithm based on kernel methods in face recognition andcompare the effects of the kernel principal component analysis and traditional lineardiscriminant method, effective information extraction using zero space to identify useful information in the space of the feature extraction before. In facial feature recognition andclassification stage, experience compares the effect of the classification and identification ofEuclidean distance and the cosine angular distance, at last,apply zero space-based kernelprincipal component analysis, linear discriminant analysis to the image after face pretreatmentas well as Euclidean distance and cosine angle distance comparison. A large number ofexperiments show that zero space-based face recognition re-use the useful information theconventional method excluded where it can enhance the effectiveness of the facial features,and the recognition rate of cosine angle distance methods is better than Euclidean distance,and it gives full play to the advantages of the cosine angle distance, obtain a satisfactoryrecognition rate.