基于统计特征的人脸识别及其光照.doc
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基于统计特征的人脸识别及其光照,摘 要人脸识别是图像处理、模式识别和人工智能研究的重点领域之一，其目的是利用计算机根据人脸的特征来鉴别人物的身份，在商业、安全、身份认证、法律执行方面具有广泛的应用。基于统计特征的人脸识别是最受关注的人脸识别技术之一，它克服了其它人脸识别方法的种种缺点，利用完备的统计学知识，根据人脸的统计特征就可有效地进行人脸识别。但...
内容介绍
此文档由会员 违规屏蔽12 发布
摘 要
人脸识别是图像处理、模式识别和人工智能研究的重点领域之一，其目的是利用计算机根据人脸的特征来鉴别人物的身份，在商业、安全、身份认证、法律执行方面具有广泛的应用。基于统计特征的人脸识别是最受关注的人脸识别技术之一，它克服了其它人脸识别方法的种种缺点，利用完备的统计学知识，根据人脸的统计特征就可有效地进行人脸识别。但是由于人脸模式的复杂性和多变性，在姿态、光照和表情等条件变化下人脸识别率会严重下降。因此，在特征提取中注重鲁棒性，同时兼顾识别效率的人脸特征提取技术是当前研究的热点。
本文主要针对人脸特征提取技术进行了研究，研究的重点是将全局特征方法与局部特征提取方法相结合，目的是在提高人脸识别率的基础上，针对姿态、光照和表情等条件变化，进一步提高人脸特征提取和识别算法的鲁棒性。本文的主要工作和创新点如下：
(1) 在研究了局部二值模式算法的基础上，将其与主成分分析算法相结合，提出了一种基于(2D)2PCA-LBP的人脸识别方法。该方法在提取了人脸纹理特征信息的基础上，用(2D)2PCA方法进行降维。LBP算法具有旋转不变性，对光照变化和姿态变化具有一定的鲁棒性；(2D)2PCA是PCA算法的改进，可以对图像进行最大程度的降维。实验结果表明，该算法可以提高人脸识别率，并且对光照、姿态和表情变化有一定的鲁棒性。
(2) 研究了压缩传感算法，针对人脸识别对遮挡、表情和光照等因素的鲁棒性问题，提出了一种基于PCA特征基压缩传感算法的人脸识别方法。该方法首先利用(2D)2PCA方法将人脸图像变换到PCA特征域，将提取的图像行列两个方向的特征作为压缩传感算法的超完备基；然后通过求解最小化l1范数，寻求图像在该超完备基上的稀疏表示，以得到一组最优的稀疏系数来重构各类图像，通过求取测试图像与重构图像的最小残差进行分类识别。该方法突破了传统方法仅用一类训练样本进行识别的缺陷，将所有训练样本同时用来进行分类，分类效果得到改善，识别率显著提高；同时，将时间复杂度降低到了线性阶。研究表明，只要训练样本足够充分，就能有效地表示测试样本的所有情况，在姿态、光照、表情变化比较大的情况下，识别率得到明显改善。
关键词 人脸识别；主成分分析法；局部二值模式；压缩传感；稀疏表示
Abstract
Face recognition is one of the most important research fields of image processing, pattern recognition and artificial intelligence, its purpose is to identify the identity of the people using computer through face feature. It has a wide range of applications, including commerce, security, person verification, and law enforcement. Face recognition based on statistical characteristics is now one of the most concerned face recognition technology by the researchers, it overcomes the shortcomings appeared in the process of traditional face recognition methods, using statistical knowledge can be effective for face recognition. However, since face patterns are complicated and multiform, the face recognition rate will face sharp decline under various conditions, such as changing illumination, pose and facial expression, which are very difficult to represent face effectively. Therefore, face recognition methods with robustness and efficiency are hotspots in recent studies.
The thesis studies on feature extraction problem in face recognition, and the focuses are the combination of global feature and local feature. Our goal is to further improve the robustness with varying expression, illumination and shadow while improving the face recognition rate at the same time. The main work and innovations are as follows:
1. This paper analyzes LBP algorithms, then combines it with PCA algorithm, presents a (2D)2PCA-LBP algorithm of face recognition. This method first extracts facial texture feature, then uses (2D)2PCA algorithm to reduce its dimension. The reason is that LBP algorithm has a characteristic of rotation invariance, which is robustness to illumination changing and pose variation. (2D)2PCA algorithm is the improvement of PCA, using this method, the image can reach the maximum degree of dimensionality reduction. The experiment results show that the algorithm can improve the face recognition rate, especially to the image with illumination changing, pose variation and facial expression, the recognition rate improve significantly.
2. The thesis studies the compressed sensing method in-depth. In order to solve the robustness problem with block, expression and illumination in face recognition system, we propose a face recognition method based on PCA-based compressed sensing algorithm. Utilizing (2D)2PCA transform to extract image features in both row and column directions and reducing the dimension. A projection matrix is constructed to identify the face features, considering these features to form an over complete dictionary. By solving the l1 norm minimization, seeking out the sparsest representation of images based on the dictionary to obtain a set of optimal sparse coefficients, which are used to recover the train images, compute the residuals between test and train images for face recognition. The method breaks through the characteristics of traditional method using only one class for recognition, we use all the training set for classification. The classifications results are improved and the time complexity is reduced to linear order, in the same time, the recognition rate improves..
人脸识别是图像处理、模式识别和人工智能研究的重点领域之一，其目的是利用计算机根据人脸的特征来鉴别人物的身份，在商业、安全、身份认证、法律执行方面具有广泛的应用。基于统计特征的人脸识别是最受关注的人脸识别技术之一，它克服了其它人脸识别方法的种种缺点，利用完备的统计学知识，根据人脸的统计特征就可有效地进行人脸识别。但是由于人脸模式的复杂性和多变性，在姿态、光照和表情等条件变化下人脸识别率会严重下降。因此，在特征提取中注重鲁棒性，同时兼顾识别效率的人脸特征提取技术是当前研究的热点。
本文主要针对人脸特征提取技术进行了研究，研究的重点是将全局特征方法与局部特征提取方法相结合，目的是在提高人脸识别率的基础上，针对姿态、光照和表情等条件变化，进一步提高人脸特征提取和识别算法的鲁棒性。本文的主要工作和创新点如下：
(1) 在研究了局部二值模式算法的基础上，将其与主成分分析算法相结合，提出了一种基于(2D)2PCA-LBP的人脸识别方法。该方法在提取了人脸纹理特征信息的基础上，用(2D)2PCA方法进行降维。LBP算法具有旋转不变性，对光照变化和姿态变化具有一定的鲁棒性；(2D)2PCA是PCA算法的改进，可以对图像进行最大程度的降维。实验结果表明，该算法可以提高人脸识别率，并且对光照、姿态和表情变化有一定的鲁棒性。
(2) 研究了压缩传感算法，针对人脸识别对遮挡、表情和光照等因素的鲁棒性问题，提出了一种基于PCA特征基压缩传感算法的人脸识别方法。该方法首先利用(2D)2PCA方法将人脸图像变换到PCA特征域，将提取的图像行列两个方向的特征作为压缩传感算法的超完备基；然后通过求解最小化l1范数，寻求图像在该超完备基上的稀疏表示，以得到一组最优的稀疏系数来重构各类图像，通过求取测试图像与重构图像的最小残差进行分类识别。该方法突破了传统方法仅用一类训练样本进行识别的缺陷，将所有训练样本同时用来进行分类，分类效果得到改善，识别率显著提高；同时，将时间复杂度降低到了线性阶。研究表明，只要训练样本足够充分，就能有效地表示测试样本的所有情况，在姿态、光照、表情变化比较大的情况下，识别率得到明显改善。
关键词 人脸识别；主成分分析法；局部二值模式；压缩传感；稀疏表示
Abstract
Face recognition is one of the most important research fields of image processing, pattern recognition and artificial intelligence, its purpose is to identify the identity of the people using computer through face feature. It has a wide range of applications, including commerce, security, person verification, and law enforcement. Face recognition based on statistical characteristics is now one of the most concerned face recognition technology by the researchers, it overcomes the shortcomings appeared in the process of traditional face recognition methods, using statistical knowledge can be effective for face recognition. However, since face patterns are complicated and multiform, the face recognition rate will face sharp decline under various conditions, such as changing illumination, pose and facial expression, which are very difficult to represent face effectively. Therefore, face recognition methods with robustness and efficiency are hotspots in recent studies.
The thesis studies on feature extraction problem in face recognition, and the focuses are the combination of global feature and local feature. Our goal is to further improve the robustness with varying expression, illumination and shadow while improving the face recognition rate at the same time. The main work and innovations are as follows:
1. This paper analyzes LBP algorithms, then combines it with PCA algorithm, presents a (2D)2PCA-LBP algorithm of face recognition. This method first extracts facial texture feature, then uses (2D)2PCA algorithm to reduce its dimension. The reason is that LBP algorithm has a characteristic of rotation invariance, which is robustness to illumination changing and pose variation. (2D)2PCA algorithm is the improvement of PCA, using this method, the image can reach the maximum degree of dimensionality reduction. The experiment results show that the algorithm can improve the face recognition rate, especially to the image with illumination changing, pose variation and facial expression, the recognition rate improve significantly.
2. The thesis studies the compressed sensing method in-depth. In order to solve the robustness problem with block, expression and illumination in face recognition system, we propose a face recognition method based on PCA-based compressed sensing algorithm. Utilizing (2D)2PCA transform to extract image features in both row and column directions and reducing the dimension. A projection matrix is constructed to identify the face features, considering these features to form an over complete dictionary. By solving the l1 norm minimization, seeking out the sparsest representation of images based on the dictionary to obtain a set of optimal sparse coefficients, which are used to recover the train images, compute the residuals between test and train images for face recognition. The method breaks through the characteristics of traditional method using only one class for recognition, we use all the training set for classification. The classifications results are improved and the time complexity is reduced to linear order, in the same time, the recognition rate improves..
