基于多线索的人脸识别认证
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摘要
生物特征识别是一项根据人类自身固有的生物特征进行身份识别的技术,具有优于传统身份识别技术的安全性和可靠性。其中,人脸识别技术以其采集方便并可在不打扰检测对象正常活动条件下隐蔽进行的独特优势,受到广泛关注并成为研究热点。经过多年的研究和开发,人脸识别技术已取得了一定的突破,在公共安全、信息安全、金融安全等领域展现出广阔的应用前景。但是,由于人脸图像的复杂性和多样性,目前人脸识别技术的精度距离人们的期望还有一些距离,提高人脸识别正确率是一项非常重要的研究任务,同时也是一项具有挑战性难度的研究课题。
人脸识别是一个复杂的信息处理过程,在真实应用条件下的人脸识别,更是受到多种不能预先确定环境因素的影响。在认知判别中,引入多种有效的特征信息和多种理论推理方法,将有助于全面、准确地认识和区分对象。本文研究和探讨了基于多线索即多种特征信息和多种理论方法集成(融合)的人脸识别技术。主要工作成果和贡献如下：
1.提出了一种基于多线索的眼睛精确定位方法。该方法包含三个阶段由粗到细逐级精确化眼睛定位。前两个阶段,采用Viola-Jones算法进行眼睛的粗定位。本文工作的主要贡献在于第三阶段的精确定位,提出采用具有更高分辨能力的梯度组合特征和曲线波(Curvelet)特征,以适应眼睛中心周围邻域的灰度分布和边缘轮廓的描述；并提出一种重构偏差计算机制,采用重构偏差来度量候选眼睛子块图像与眼睛子块典型样本图像集的匹配相似度,进行计算对比,将基于梯度组合特征和曲线波特征的重构偏差结果进行综合,最终选定最佳的眼睛中心位置。实验结果表明,本文方法具有较高的定位精度。
2.提出了一种基于Gabor特征、Curvelet特征和LBP (Local Binary Pattern)特征融合的人脸识别方法。Gabor、Curvelet和LBP都是有成效的描述人脸的特征,它们分别从通用的和侧重于边缘轮廓的多尺度波形分解和亮度差细观纹理结构等不同特性角度,描述了对象的内蕴特性。三种特征内容具有很强的关联性和互补性,在本文提出的方法中,首先将特征相关性较强的Gabor特征和Curvelet特征在特征层上采用典型相关分析(Canonical Correlation Analysis, CCA)的原理方法进行重组融合；然后将CCA融合特征的匹配相似度与LBP特征的匹配相似度在决策层上进行融合,获得最终的匹配相似度。实验结果表明,通过将三种特征进行融合识别精度得到显著提高。
3.对于同一人脸多张图像和单张人脸图像的匹配问题,本文提出一种计算同一人脸多张图像的共性表征和匹配偏差方法。在选定特征表示后,同一人脸对象的多张图像特征数据计算得到的主方向可作为该人脸对象的共性表征。将待匹配单张图像在已知多张图像的主方向上投影,然后进行重构,其重构偏差可作为匹配偏差。通过实验测试证明本文提出的方法的有效性。
4.对于非正面人脸图像匹配问题,本文提出了一种新的人脸姿态估测方法以及两项简化校正处理：纵横比修正和选用翻转图像。在人脸姿态估测方法中,采用局部线性嵌入(Locally Linear Embedding, LLE)方法约简特征维数,采用稀疏编码和字典学习的方法进行偏转程度分级。在侧向面对摄取方向的人脸图像中,人脸的宽度方向尺寸产生缩减,本文提出了一种纵横比(宽高比)修正处理方法。当两张匹配图像的偏转方向相反且偏转角度较大时,直接进行匹配处理,匹配偏差较大,本文提出一种选用翻转图像的处理方法,即将其中一张图像左右对称翻转后再与另一张图像进行匹配。实验结果证明,所提出的姿态分级估测方法健壮,通过两项简化处理,识别效果具有明显的改善。
Biometric recognition is a recognition technology based on the inherent biometrics of human beings and is superior to the traditional identification technology in security and robustness aspects. Due to its unique advantages such as convenient data acquisition and the capability working under concealed environments without disturbing the detected objects, face recognition has drawn widespread attention and is a research hotspot now. Some technology breakthroughs had been made in the past two decades. The application prospect of face recognition is wide in the fields of public security, information security and financial security etc. However, due to the complexity and the radon variations of face images, there still is a long way to go to meet the people's expectation. Upgrading the identification performance of face recognition is still an urgent and important task in developing technology; however, it is also a challenge research issue in pattern recognition discipline.
Face recognition is a complex information processing. Face recognition in real application conditions is influenced by a variety of uncertain environment factors. In the cognition implementations, incorporating multiple effective feature information and theory methods will help system recognize and discriminate different objects comprehensively and accurately. This paper deals with the researches of face recognition technology with multiple cues. The main works and contributions are as follows:
1. An approach of precise localization of eye centers with multiple cues is proposed. This approach searches the precise localization of eye centers from coarse to fine by three stage processing. In the first two stages, Viola-Jones approach is used for the rough localization of eye centers. The main contribution of the proposed approach is the precise localization processing of eye centers in the third stage. Gradient combination features and Curvelet features were constructed and used in precise processing, both features possess higher discrimination ability in revealing the intensity distribution and edge characteristics of the neighbourhood around eye center. A rebuilt error calculation mechanism is proposed and the rebuilt errors are used for evaluating the matching similarity between a test eye patch image and the pre-constructed reference eye patch image set. The final localizations of eye centers are selected based on integrating the rebuilt error results of gradient combined based features and Curvelet based features. The experiment results show that the proposed approach achieved high localization precision.
2. A face recognition approach with feature fusion of Gabor based, Curvelet based and LBP based representations is proposed. Gabor, Curvelet and LBP are effective features successfully used for face representations. They reveal the intrinsic characteristics of the description object from different characteristics views such as general multi-scale waveform decomposition, multi-scale edge-oriented waveform decomposition and the micro texture of lightness differences. These features possess strong correlation and complementarity. In the proposed approach, the Gabor features and Curvelet features are first fused by canonical correlation analysis (CCA) on feature layer; then the matching similarity score of CCA fusion features and similarity score of LBP features are integrated on decision layer. The experiment results show that the proposed approach upgrades the recognition accuracy significantly.
3. For the matching problem of multiple face images from one person to a single face image, a novel approach is proposed. The proposed approach first analyses the multiple face images, which were taken from same person, statistically. After select a feature representation, the mean feature values and the principle directions of feature data of multiple face images from same person can be calculated. The mean feature values and the principle directions will be taken as the common attributes of the specified person. Project a single test image onto the principle directions of multiple face images, and then generate a rebuilt image with limited number terms of the principle components. The difference between the original test image and the rebuilt image is called rebuilt error. The rebuilt error is taken as matching error and the match with smallest rebuilt error is assumed the best match in processing. The experiment results demonstrate the effectiveness of the proposed approach.
4. For the non-frontal face image matching problem, a novel face pose estimation approach and two simplification processing operations are proposed. The two simplification processing operations are:aspect ratio modification and flipping image selection. In the face pose estimation process, the locally linear embedding (LLE) method is first used for the dimension reduction of features; then, sparse coding and dictionary learning methods are used for yaw degree classification. For the face image taking in side direction, the width size will be compressed, an aspect ratio (width/height ratio) modification operation is proposed in this paper. When the two matching images are oriented on opposite side and possess larger yaw angles, the matching error is larger if match the two images directly. A flipping image selection operation is proposed which is replacing one of these two images with its left-right symmetric flipping image in matching processing. The experiment results demonstrate that the proposed pose estimation approach is robust and the two simplification processing operations improve the recognition performance obviously.
人脸识别是一个复杂的信息处理过程,在真实应用条件下的人脸识别,更是受到多种不能预先确定环境因素的影响。在认知判别中,引入多种有效的特征信息和多种理论推理方法,将有助于全面、准确地认识和区分对象。本文研究和探讨了基于多线索即多种特征信息和多种理论方法集成(融合)的人脸识别技术。主要工作成果和贡献如下：
1.提出了一种基于多线索的眼睛精确定位方法。该方法包含三个阶段由粗到细逐级精确化眼睛定位。前两个阶段,采用Viola-Jones算法进行眼睛的粗定位。本文工作的主要贡献在于第三阶段的精确定位,提出采用具有更高分辨能力的梯度组合特征和曲线波(Curvelet)特征,以适应眼睛中心周围邻域的灰度分布和边缘轮廓的描述；并提出一种重构偏差计算机制,采用重构偏差来度量候选眼睛子块图像与眼睛子块典型样本图像集的匹配相似度,进行计算对比,将基于梯度组合特征和曲线波特征的重构偏差结果进行综合,最终选定最佳的眼睛中心位置。实验结果表明,本文方法具有较高的定位精度。
2.提出了一种基于Gabor特征、Curvelet特征和LBP (Local Binary Pattern)特征融合的人脸识别方法。Gabor、Curvelet和LBP都是有成效的描述人脸的特征,它们分别从通用的和侧重于边缘轮廓的多尺度波形分解和亮度差细观纹理结构等不同特性角度,描述了对象的内蕴特性。三种特征内容具有很强的关联性和互补性,在本文提出的方法中,首先将特征相关性较强的Gabor特征和Curvelet特征在特征层上采用典型相关分析(Canonical Correlation Analysis, CCA)的原理方法进行重组融合；然后将CCA融合特征的匹配相似度与LBP特征的匹配相似度在决策层上进行融合,获得最终的匹配相似度。实验结果表明,通过将三种特征进行融合识别精度得到显著提高。
3.对于同一人脸多张图像和单张人脸图像的匹配问题,本文提出一种计算同一人脸多张图像的共性表征和匹配偏差方法。在选定特征表示后,同一人脸对象的多张图像特征数据计算得到的主方向可作为该人脸对象的共性表征。将待匹配单张图像在已知多张图像的主方向上投影,然后进行重构,其重构偏差可作为匹配偏差。通过实验测试证明本文提出的方法的有效性。
4.对于非正面人脸图像匹配问题,本文提出了一种新的人脸姿态估测方法以及两项简化校正处理：纵横比修正和选用翻转图像。在人脸姿态估测方法中,采用局部线性嵌入(Locally Linear Embedding, LLE)方法约简特征维数,采用稀疏编码和字典学习的方法进行偏转程度分级。在侧向面对摄取方向的人脸图像中,人脸的宽度方向尺寸产生缩减,本文提出了一种纵横比(宽高比)修正处理方法。当两张匹配图像的偏转方向相反且偏转角度较大时,直接进行匹配处理,匹配偏差较大,本文提出一种选用翻转图像的处理方法,即将其中一张图像左右对称翻转后再与另一张图像进行匹配。实验结果证明,所提出的姿态分级估测方法健壮,通过两项简化处理,识别效果具有明显的改善。
Biometric recognition is a recognition technology based on the inherent biometrics of human beings and is superior to the traditional identification technology in security and robustness aspects. Due to its unique advantages such as convenient data acquisition and the capability working under concealed environments without disturbing the detected objects, face recognition has drawn widespread attention and is a research hotspot now. Some technology breakthroughs had been made in the past two decades. The application prospect of face recognition is wide in the fields of public security, information security and financial security etc. However, due to the complexity and the radon variations of face images, there still is a long way to go to meet the people's expectation. Upgrading the identification performance of face recognition is still an urgent and important task in developing technology; however, it is also a challenge research issue in pattern recognition discipline.
Face recognition is a complex information processing. Face recognition in real application conditions is influenced by a variety of uncertain environment factors. In the cognition implementations, incorporating multiple effective feature information and theory methods will help system recognize and discriminate different objects comprehensively and accurately. This paper deals with the researches of face recognition technology with multiple cues. The main works and contributions are as follows:
1. An approach of precise localization of eye centers with multiple cues is proposed. This approach searches the precise localization of eye centers from coarse to fine by three stage processing. In the first two stages, Viola-Jones approach is used for the rough localization of eye centers. The main contribution of the proposed approach is the precise localization processing of eye centers in the third stage. Gradient combination features and Curvelet features were constructed and used in precise processing, both features possess higher discrimination ability in revealing the intensity distribution and edge characteristics of the neighbourhood around eye center. A rebuilt error calculation mechanism is proposed and the rebuilt errors are used for evaluating the matching similarity between a test eye patch image and the pre-constructed reference eye patch image set. The final localizations of eye centers are selected based on integrating the rebuilt error results of gradient combined based features and Curvelet based features. The experiment results show that the proposed approach achieved high localization precision.
2. A face recognition approach with feature fusion of Gabor based, Curvelet based and LBP based representations is proposed. Gabor, Curvelet and LBP are effective features successfully used for face representations. They reveal the intrinsic characteristics of the description object from different characteristics views such as general multi-scale waveform decomposition, multi-scale edge-oriented waveform decomposition and the micro texture of lightness differences. These features possess strong correlation and complementarity. In the proposed approach, the Gabor features and Curvelet features are first fused by canonical correlation analysis (CCA) on feature layer; then the matching similarity score of CCA fusion features and similarity score of LBP features are integrated on decision layer. The experiment results show that the proposed approach upgrades the recognition accuracy significantly.
3. For the matching problem of multiple face images from one person to a single face image, a novel approach is proposed. The proposed approach first analyses the multiple face images, which were taken from same person, statistically. After select a feature representation, the mean feature values and the principle directions of feature data of multiple face images from same person can be calculated. The mean feature values and the principle directions will be taken as the common attributes of the specified person. Project a single test image onto the principle directions of multiple face images, and then generate a rebuilt image with limited number terms of the principle components. The difference between the original test image and the rebuilt image is called rebuilt error. The rebuilt error is taken as matching error and the match with smallest rebuilt error is assumed the best match in processing. The experiment results demonstrate the effectiveness of the proposed approach.
4. For the non-frontal face image matching problem, a novel face pose estimation approach and two simplification processing operations are proposed. The two simplification processing operations are:aspect ratio modification and flipping image selection. In the face pose estimation process, the locally linear embedding (LLE) method is first used for the dimension reduction of features; then, sparse coding and dictionary learning methods are used for yaw degree classification. For the face image taking in side direction, the width size will be compressed, an aspect ratio (width/height ratio) modification operation is proposed in this paper. When the two matching images are oriented on opposite side and possess larger yaw angles, the matching error is larger if match the two images directly. A flipping image selection operation is proposed which is replacing one of these two images with its left-right symmetric flipping image in matching processing. The experiment results demonstrate that the proposed pose estimation approach is robust and the two simplification processing operations improve the recognition performance obviously.
引文
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