人脸表情识别系统开发毕业论文.doc

本科毕业论文
(科研训练、毕业设计)
题目:人脸表情识别系统开发
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学院:软件学院
专业:软件工程
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指导教师(校内): 职称:讲师
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摘要
在过去几十年内,研究者已经提出了很多用于面部表情识别的方法。目前的人脸面脸表情识别方法可以分为两种:基于局部特征和基于整体特征的。基于局部特征的人脸面部表情识别是利用每个人的面部特征(眉毛、眼睛、鼻子、嘴巴和面部轮廓等)的位置、大小及其相互位置的不同进行特征提取,达到人脸面部表情识别的目的。
本文基于对表情识别系统的开发,而对人脸表情识别技术进行了研究和探讨,提出了识别人脸七种基本表情(愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊奇)的方法。
通过使用PCA方法,实现一套识别率较高的图像人脸表情识别系统。该系统主要实现特征提取,表情识别两大功能。
图像人脸特征定位系统是以matlab 为开发平台,使用PCA算法进行开发设计。
关键词:特征提取表情分类基于特征脸的主分量分析(PCA) MATLAB
Abstract
in recent years ,Facial expression recognition by the widespread attention it with fingerprint identification, Retinal identification belonging to the same biometric identification areas. Image Facial Expression Recognition of face recognition is an important element. Accurate positioning is correct characteristic facial expressions to identify the necessary conditions.
In the past few decades, researchers have made a lot of facial expression recognition method. The Face Facial expression recognition can be divided into two methods : the film based on the local characteristics and the overall characteristic. Based on the characteristics of the partial facial expression recognition is the use of each person's facial features (eyebrows, eyes, nose, mouths and facial contours, etc.) location, size and location of different mutual feature extraction, achieve Facial expression recognition purposes.
Gray also be considered brightness simply means that the color depth. And the gradient is a little gray change of speed.
Based on the facial recognition system, we did a face recognition technology for the study and exploration, Recognition of the seven basic facial expressions (anger, dislike, fear, happiness, expressionless, sadness and surprise) method.
Through the use of PCA . we achieved a higher recognition rate of Facial Expression Image Recognition System. The system main feature extraction, and the two major functions are expression recognition. Facial features images positioning system based on Matlab platfo