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Research and Implementation on Multi-angle Face Detection Technology Based on Continuous Adaboost Algorithm

Author: LiaoWenJun
Tutor: SunZhiXin
School: Nanjing University of Posts and Telecommunications
Course: Computer Software and Theory
Keywords: Continuous adaboost Multi-angle face detection Human peripheral objects Feature Extraction
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 70
Quote: 0
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Abstract


With the continuous development of digital image processing technology, and intelligent learning algorithm, face detection technology is more and more applications in the field of video surveillance, human-computer interaction, as well as e-commerce; refers to the so-called face detection process from a static image or dynamic frame of video images of the human face object from the background range segmentation out and designees face the process of regional. Currently, most of the mature face detection rules sample for positive face detection, face detection, multi-angle studies and practical examples of small, so researchers study how to conduct efficient and accurate multi-angle face detection more and more the focus of attention problems. Firstly, face detection technology have someone review and summarize be divided into feature-based face detection method based on template matching face detection and face detection method based on statistical three categories, and pointed out the most widely, the best accuracy and efficiency, which leads to the core algorithm - Adaboost algorithm based on statistical face detection method based on the integral image features; Secondly, Adaboost algorithm applied basic technology - Haar feature its expansion and integral image technology, to lay the foundation for using Adaboost algorithm for face detection; same time, also summarizes the Real Adaboost face detection process, and continuous Adaboost algorithm improvements The progress done research and exposition, build and the division weak classifiers based on the multiple thresholds to build two weak classifiers improved method that weak classifier based on a lookup table, and noted that they are defective in training speed. Scene for multi-angle face detection, research and implementation of a multi-angle face divided multi-angle face is divided into 84 kinds, and classifier learning through the sample and study the characteristics of the Haar features will need to be reduced to 12, which greatly improves the speed of the classifier training; same time, due to the requirements of the overall project, the overall face detection process improvements, increase image type detection, skin color detection, absolute position detection detected and the relative position of the four steps, and the eventual realization of a multi-angle face detection system based on Real Adaboost (CAMFDS). Achieve continuous Adaboost algorithm for multi-angle face detection system (CAMFDS) using the MIT public face training sample set as a training sample face images from the Internet in 2157 as a test sample set for color and gray degrees, with eyes closed and eyes open, normal and small eyes, and with or without glasses situations such classification test, the test results show that the lowest face detection accuracy of 88.9%, the lowest recall rate of 80.0%, execution efficiency statistics per the images total process time-consuming 708.7 milliseconds, which contains the pretreatment time-consuming, Adaboost algorithm to perform time-consuming, and improve the process time-consuming. Based on continuous Adaboost algorithm for multi-angle face detection system as the characteristics of human peripheral object extraction system is an important component in the subsystem, overall system provides an accurate and efficient face positioning, and lay a solid foundation for the smooth implementation of the follow-up detection module human peripheral object feature extraction system acceptance successfully closed items, the overall project, a patent the invention has been disclosed.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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