Research And Implementation On Multi-angle Face Detection Technology Based On Continuous Adaboost Algorithm

With the continuous development of the digital image processing technology and intelligentlearning algorithms, Face Detection technology is increasingly being applied to video surveillance,human-computer interaction and e-commerce and other fields. Face detection is a process ofcarving out the face objects from the static images or dynamic video frame background, andspecifying the range of face area. Currently, the mature detection technologies are mostly used forthe frontal face detection, while the research and practical cases for multi-angle face detection areinfrequent. Therefore, how to efficiently and accurately detect the multi-angle face is increasinglybecoming the focus of researchers'attention.Firstly, the paper summarizes the existed face detection technology, and divides it into threecategories: Feature-based face detection method, Based on template matching face detectionmethod and Statistics-based face detection method. The paper points out that, at present the mostwidely used, the best accuracy and efficiency method is based on image features in statistics-basedface detection method, and this paper introduces a core algorithm-Adaboost algorithm. Secondly,this paper carries on deep research about basic technology related to adaboost algorithm-Haarfeatures and extensions and integral image technology, for the use of adaboost face detectionalgorithm to lay the foundation; at the same time, generalizes the process of continuous adaboostface detection algorithm in the application. This paper researchs and explains the progress of theimprovement of the continuous adaboost algorithm. This paper also indicates that the Look-uptable-based weak classifiers building method and multiple threshold division-based weak classifiersbuilding method are two kinds of improvement methods of weak classifiers and demonstrates theirshortcomings in the training speed. For the multi-angle face detection application scene, this paperresearchs and implements multi-angle face division, divides the multi-angle face into 84 kinds; Atthe same time, by studying the characteristics of haar features, we reduce the classifiers to 12formed by sample study. As the overall project requirements, this paper improves the overallprocess of face detection, increases four steps in the process: Image type detection, Color detection,The absolute position detection and The relative position detection, and finally, achieves acontinuous adaboost algorithm-based multi-angle face detection system (CAMFDS).CAMFDS uses MIT open face training set as training samples, 2157 face images from theInternet as a test sample set, and tests the classification of color and grayscale, eyes closed and eyesopen, normal and small eyes, with or without glasses cases. The result shows that face detection's accuracy achieve 88.9% at least, recall rate achieve 80.0% at least, the total process of each imagetakes 708.7ms that includes the time-consuming of pre-processing, improved process and adaboostalgorithm execution. CAMFDS as an important component subsystem of Human peripheral objectfeature extraction system, provides a precise and efficient positioning of the human face, and lays asolid foundation for the successful implementation of the follow-up test modules. Human peripheralobject feature extraction system has been successfully checked and accepted. The overall projecthas been finished,and a related invention patent has been published.