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Color face image detection and local feature location identification

Author: LiYongFang
Tutor: WuYue
School: University of Electronic Science and Technology
Course: Computer Software and Theory
Keywords: Face Detection Skin color model Face Recognition Geometric characteristics PCA Harr Wavelet
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 59
Quote: 0
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Abstract


Face detection and recognition technology is challenging popular research direction is a key application field of computer vision technology, has great potential in economic, security, social security, and military value. Face including face detection, face tracking, face recognition, including face detection and face recognition is a hot research topic. In this paper, in order to achieve a usable and practical face detection face detection and recognition system based on the Web as a goal in the study based on face detection and recognition of basic theory and key technology, focused on the color still images and static gray-scale image of the face recognition problem. Face recognition used still images from a YALE and ORL gray face of the database. Face detection, in-depth analysis of the entire process based on skin color model. First, to analyze and compare the clustering of color in various commonly used color space YCbCr space finally selected skin model. In this paper, YCbCr nonlinear optimization algorithm, makes the complexion good clustering in the case of over-exposure. Then use the complexion Gaussian model derived skin color likelihood map, using the optimum threshold and the adaptive threshold method of combining the obtained binarized threshold, the initial realization face and background segmentation. Different situation for many people face images and single face image, this paper presents a face screening algorithm. Finally, we propose to use mosaic template fuzzy matching method to further fine-election results of the initial screening. In face recognition, face recognition method based on the geometric characteristics and features of the face. Algorithm based on the geometric characteristics, grayscale integral projection method to obtain facial features relative coordinates as a feature vector addition introduced gender identity (bangs, bearded) as identification basis. The eigenface algorithm introduces preliminary dimensionality reduction HARR wavelet transform on face images, then KL orthogonal transforms its Eigenface face matrix projection to the features of the face sheets into the subspace projection coefficients i.e. feature vectors. The last face detection and recognition system based on the Web, and gives a detailed design. Experimental results are based on the system. The experiments show that the system with posture and light with strong adaptability.

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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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