【作者】 高羽佳；

【导师】 李孟歆； 赵敏；

【作者基本信息】 沈阳建筑大学 ， 控制工程， 2011， 硕士

【摘要】 作为生物特征识别中最自然最直接的手段,人脸识别技术受到越来越多的关注。基于可见光图像的人脸识别系统目前已取得一些成果,但是其不仅不适合于对伪装脸的识别,而且性能还受到光照变化的影响,尤其当照明非均匀、光照昏暗或者在户外时,识别率会明显降低。基于红外的人脸图像识别系统虽然对光照的鲁棒性较好,但是当待识别对象戴有眼镜时,系统识别性能会骤然下降。基于上述问题,本文将如何利用图像融合技术来提高人脸识别系统的整体识别性能作为研究对象。将基于奇异值分解(SVD)图像分层的技术应用于多模式图像融合,然后基于主成分分析(PCA)方法利用融合图像进行人脸识别。主要内容如下:(1)研究实现人脸图像归一化的方法。首先实现图像的灰度归一化,然后进行人眼定位,利用人眼位置信息实现人脸图像的几何归一化,得到标准人脸。(2)研究人脸图像SVD分解和基于PCA的人脸识别算法。介绍了SVD分解的原理以及SVD分解在人脸识别中的应用,并详细阐述了基于奇异值分解的人脸特征提取的方法以及基于PCA实现人脸识别的具体方法和步骤。(3)研究基于红外与可见光图像融合的人脸识别方法,主要包括两个部分:一是基于像素级融合的人脸识别研究;二是基于决策级融合的人脸识别效果研究。前者主要讨论基于小波分解和基于奇异值分解(SVD)的红外人脸图像和可见光人脸图像的融合技术,然后将采用两种融合处理得到的结果图像应用于人脸识别;后者实现可见光图像和红外图像两种识别结果的决策级融合。论文对比分析了几种融合算法的实验结果,并验证了该算法的有效性。更多还原

【Abstract】 Most recently, as the most natural and explicit approach for biological feature recognition, face recognition has attracted increasing attentions. Although face recognition approaches based on the visual light spectrum have gained some success,it not only unsuitable for the disguised face, but also the performance may be affected by the change of the lighting, especially when the lighting is uneven or dim, or in the outdoor circumstances, the recognition rate may decline obviously. Face recognition system based on infrared (IR) have better performance in terms of robustness.However, when the people put on the glasses, the performance of system recognition shows a shape decrease. Based on all the issues mentioned above, in the thesis, the study focus on multi spectral image fusion technology that is utilized to improve the overall performance of the Face recognition system. the image slicing technology based on singular value decomposition (SVD) is used to the fusion of multi-pattern image, then the fusion image is used to recognize the faces based on the principal component analysis (PCA) method.(1)Approach of normalization for face image the is studied. Firstly, gray normalization of the face image is realized, then the eye location process is carried out, the standard face is obtained by using geometric normalization the based on the position information of the eyes.(2)Further, the singular value decomposition (SVD) of and the face recognition algorithm based on PCA are studied. Principles and applications of the SVD for the face image are introduced, the method of obtaining features of face and steps for face recognition based on PCA are illustrated as well.(3)Image slicing approaches is utilized to realize the fusion of visible and light infrared based on the energy of the image. Firstly, the SVD decomposition of layers according to different energy , i.e., low, high and ultrahigh resolution layers ,respectively. Besides, in each layer, the fusion strategies is used according to the different performance features. Last, the face recognition algorithm is realized through the fusion of the image. In the thesis, several experimental results of fusion algorithm are compared, the effectiveness of the algorithm is verified as well.更多还原