基于PCA和AdaBoost的改进人脸识别算法研究  

An Improved Face Recognition Algorithm Based on PCA and AdaBoost

作　　者：张旭东[1] 徐和根[1] 

机构地区：[1]同济大学电子与信息工程学院,上海201804

出　　处：《中国电子商情（通信市场）》2013年第3期69-76,共8页

摘　　要：利用人脸作为特征的生物识别系统是近年来模式识别和图像处理领域的研究热点之一。介绍了一种改进的人脸识别算法。算法以主成分分析（PCA）算法作为主体，以AdaBoost算法作为辅助，把以投影后人脸特征空间中的欧式距离作为识别的主要评判依据。与传统人脸识别系统相比，新算法可以避免系统在识别前进行人脸检测的巨大运算量，并有效区分人脸和非人脸图像，提高运算效率和识别精度。仿真结果表明，这种改进的算法硬件资源占用少，运算时间短，更适合在嵌入式平台上实现。In recent years, the biometric identification system using human face as a biometric characteristic is one of the highlights in agro-scientific research in the field of pattern recognition and image processing. An improved face recognition algorithm is proposed in the present paper. In this algorithm, the principle component analysis （PCA） algorithm is the main body, the AdaBoost algorithm is the auxiliary, the Euclidean distance in projected face feature space is the key indicator of recognition. Compared with the traditional face recognition system, the algorithm can avoid the huge computational complexity in the face detection phase before recognition phase. It also can distinguish between face and non-face images effectively and improve the operational efficiency and accuracy. The simulation results show that this improved algorithm has less hardware resource usage, shorter operation time and it is more suitable to implement on the embedded platform compared with the traditional face recognition system.

关 键 词：人脸识别 人脸检测 主成分分析 MATLAB仿真中图法分类号TP391 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]