基于人脸图像的性别识别与年龄估计研究
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摘要
近年来,基于人脸图像的生物特征识别研究取得了巨大的发展,与其它的生物特征相比,人脸特征具有自然性、方便性和非接触性等优点,使其在安全监控、身份验证、人机交互等方面具有巨大的应用前景。基于人脸图像的性别识别及年龄估计是指根据人的脸部图像判别其性别及估计其年龄的模式识别问题。性别识别和年龄估计因其在身份认证、人机接口、视频检索以及机器人视觉中存在潜在的应用,成为当前计算机视觉和模式识别领域中的研究课题之一,备受关注。
在生活中,人们能很容易地根据人脸来判别其性别,也能大致估计年龄,但是让计算机识别并不容易。来自心理学、计算机视觉、模式识别、人工智能等各领域的研究人员做了大量的研究工作,但赋予计算机与人类同样的能力仍然是富有挑战性的研究课题。本文在国内外已有的研究基础上,对性别识别和年龄估计做了较深入的研究。
用于人脸性别识别和年龄估计的特征提取算法仅需要提取人脸区域的特征,人脸区域检测是人脸性别识别和年龄估计的一个必要环节,其准确性会直接影响到特征提取的有效性。因此研究了基于Adaboost学习算法的人脸检测方法,采用多级分类器结构和金字塔结构实现多姿态人脸检测并提高了检测速度。实验结果表明该方法是快速有效的。
为归一化人脸的大小,提出基于Adaboost和快速辐射对称变换的双眼定位方法,利用快速辐射对称变换寻找特征点,用基于Adaboost的方法检测眉毛眼睛区域以缩小搜索双眼的区域,减少其它特征点对双眼定位的影响,再利用眼珠模板和双眼的几何特征即可准确地定位双眼。采用基于主动外观模型的方法定位脸部特征点以提取人脸图像的局部特征。
性别识别和人脸识别一样,都需要寻找有效而又稳定的面部特征,只是最终识别类别不同。本文研究了人脸性别识别的特征提取和分类方法,如局部二进制模式方法(LBP)、神经网络方法、Adaboost方法、SVM方法,以及直接把人脸图像的灰度值作为特征的性别识别方法。进一步研究了实际应用环境对人脸性别识别性能的影响,即把人脸检测和性别识别两者连接起来考察人脸性别识别的性能,研究了人脸图像的尺度大小对性别识别性能的影响。
性别识别方法根据特征提取方法不同可以分为:基于整体特征的方法和基于局部特征的方法。两类特征对识别都是必要的,且能互相补充在识别过程中共同起作用。为提高识别率,提出了采用AdaBoost算法提取脸部整体特征,主动外观模型提取局部特征(脸部几何特征),融合局部与整体特征后用支持向量机进行分类的方法。本文在一个由AR、FERET、CAS-PEAL、网上收集和实验室自行采集所共同组成的,包含21,300余张人脸的数据库上,进行了大量有意义的实验。实验结果显示,融合了整体特征和局部特征后,识别率比基于(单独)整体特征的、基于(单独)局部特征的有很大的提高,达到了90%以上。实验结果还表明,识别过程中由于有了几何特征的参与,对于脸部光照、姿态等变化表现出了更强的鲁棒性和更好的识别结果。
大多数性别分类方法是对整幅人脸图像提取特征,相对的,各脸部子区域受脸部表情变化的影响更小。为提高脸部表情变化时的性别识别率,提出一种基于子区域特征的性别识别方法。本文详细比较了脸部各子区域对性别识别的贡献大小,包括眼睛、鼻子、嘴巴、下巴、左眼区域,脸内部区域(包括眼睛、鼻子、嘴巴和下巴)、以及整个人脸区域(带头发的人脸区域)。用FERET和CAS-PEAL两个人脸数据库进行实验,实验结果表明人脸部各子区域同样具有足够的与性别相关的信息,用单个子区域进行性别识别的正确率可以达到80%以上。通过融合脸部对性别识别贡献较大的子区域,利用各子区域包含的脸部特征信息是互补的,进一步提高了性别识别准确率。
提出利用能互相补充的脸部信息,即脸部灰度图像、脸部Gabor小波特征、以及眼睛区域,来提高年龄估计的准确率。用Gabor小波提取脸部特征的方法,Gabor小波具有多方向和多尺度选择的特性,并且能够获取对应于空间和频率的局部结构信息,对于图像的亮度和对比度变化以及人脸姿态变化具有较强的鲁棒性。为进一步提高准确率,针对男性和女性在人脸年龄特征表现上的差异,以性别信息作为年龄估计的先验知识。用支持向量机对各脸部特征分别进行年龄估计,融合后得到年龄识别结果,并对融合方法进行了改进。
Recently, Biometrics has been applied into pattern recognition including face recognition, fingerprint recognition, iris recognition, palm print recognition etc. Compared with other biometric personal authentication technologies, face recognition technology is natural, convenient and non-contact. These advantages make it be applied widely in security surveillance, verification, man-machine conversation and so on. Gender classification and age estimation is an attempt to give the computers the ability to discriminate the gender information and estimation age from a face image. Gender classification and age estimation has become the hotspot of computer vision and pattern recognition for its important application prospects in identity recognition, man-machine interface, video index and robot vision and so on. However, it is still one of the most challenging problems in the fields of computer vision.
Since feature extraction algorithms used by gender classification and age estimation just need to extract the features of face area, face detection is a necessary step in automatic gender classification and age estimation systems. Moreover, the accuracy of face detection would affect the effectivity of feature extraction. So multi-view face detection method based on Adaboost learning algorithm is applied, and its cascaded classifier structure and pyramid architecture both improve the speed of detection. The experimental results show the face detection method is rapid and effective.
In order to normalize the size of face image, an eye location method based on Adaboost and fast radial symmetry transform is proposed. Firstly, fast radial symmetry transform can search feature points rapidly. Secondly, an eyebrow region detection algorithm based on Adaboost is presented to reduce the range of searching eyes, which can decrease the influence of other feature points on eye location. Finally, the precise location of eyes can be obtained by using pupil model and geometrical features of eyes. Furthermore, the results of eye location are used to initialize active appearance model, which is applied to locate facial feature points so as to extract the local features of face images.
Gender classification and face recognition both need to find effective and stable facial features, but ultimately identify the different patterns. In this paper, we compared those methods which used in gender classification, including local binary pattern, neural network, Adaboost, support vector machines (SVM), and image pixels as input. We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with different combinations of automatic face detection, face alignment and gender classification, and show the classification rates for different face images sizes.
Based on the type of features used, previous studies can be broadly classified into two categories: appearance feature-based (global) and geometric feature-based (local). The former finds the decision boundary directly from training images while the latter is based on geometric features such as eyebrows thickness, nose width, etc. The local and global features supplement each other under some conditions. In this paper, a novel gender classification method based on frontal face images is presented. In this work, the global features are extracted using AdaBoost algorithm. Active Appearance Model locates 83 landmarks, from which the local features are characterized. After the fusion of the local and global features, the mixed features are used to train support vector machine classifiers. This method is evaluated by the recognition rates over a mixed face database containing over 21,300 images from 4 sources (AR, FERET, CAS-PEAL, WWW and a database collected by the lab). Experimental results show that the hybrid method outperforms the unmixed appearance- or geometry-feature based methods and achieve a classification rate over 90%.
In the past, most computational models of gender classification use global information (the whole face image) giving equal weight to all areas of the face, irrespective of the importance of internal features. Intuitively, we argue that smaller facial regions, if judiciously selected, would be less sensitive to expression variations and may lead to better overall performance. We evaluate the significance of different facial regions for gender perception. Our work on gender classification is one of the first attempts to report a detailed evaluation of the significance of different facial regions including the whole face (including hairline), the internal face, the upper region of face, the lower region of face, the left eye, the nose, and the mouth. Considering the significance of facial regions, we propose a fusion-based method, combining the classification results of three facial regions, for improving the robustness to facial expressions.
We notice that most computational models of the existing age estimation methods consider only the entire face as a global feature, they do not take into account just what other regions of the face as local features. We propose a novel method for age estimation that combines information from multiple facial features for improving accuracy and robustness. The facial features that we consider are the grayscale image of the face, the Gabor wavelet representation of the face, and the eyes. The Gabor wavelet representations are robust for illumination and expressional variability, so are used widely in facial feature modeling in recent years. The eyes are essentially unaffected by beards and mustaches and quite robust to facial expressions and occlusions. Moreover, the area around the eyes was found to be the most significant for the task of age estimation. The idea is to use complementary information for improving overall performance. Further, we propose a fusion method that further improves the accuracy. Each feature provides an opinion on the claim in terms of a confidence value which is calculated by SVMs. The confidence values of all the three features are then fused for final age estimation. The proposed fusion method works quite well and yields a significant improvement in age estimation over that achievable with any single feature.
在生活中,人们能很容易地根据人脸来判别其性别,也能大致估计年龄,但是让计算机识别并不容易。来自心理学、计算机视觉、模式识别、人工智能等各领域的研究人员做了大量的研究工作,但赋予计算机与人类同样的能力仍然是富有挑战性的研究课题。本文在国内外已有的研究基础上,对性别识别和年龄估计做了较深入的研究。
用于人脸性别识别和年龄估计的特征提取算法仅需要提取人脸区域的特征,人脸区域检测是人脸性别识别和年龄估计的一个必要环节,其准确性会直接影响到特征提取的有效性。因此研究了基于Adaboost学习算法的人脸检测方法,采用多级分类器结构和金字塔结构实现多姿态人脸检测并提高了检测速度。实验结果表明该方法是快速有效的。
为归一化人脸的大小,提出基于Adaboost和快速辐射对称变换的双眼定位方法,利用快速辐射对称变换寻找特征点,用基于Adaboost的方法检测眉毛眼睛区域以缩小搜索双眼的区域,减少其它特征点对双眼定位的影响,再利用眼珠模板和双眼的几何特征即可准确地定位双眼。采用基于主动外观模型的方法定位脸部特征点以提取人脸图像的局部特征。
性别识别和人脸识别一样,都需要寻找有效而又稳定的面部特征,只是最终识别类别不同。本文研究了人脸性别识别的特征提取和分类方法,如局部二进制模式方法(LBP)、神经网络方法、Adaboost方法、SVM方法,以及直接把人脸图像的灰度值作为特征的性别识别方法。进一步研究了实际应用环境对人脸性别识别性能的影响,即把人脸检测和性别识别两者连接起来考察人脸性别识别的性能,研究了人脸图像的尺度大小对性别识别性能的影响。
性别识别方法根据特征提取方法不同可以分为:基于整体特征的方法和基于局部特征的方法。两类特征对识别都是必要的,且能互相补充在识别过程中共同起作用。为提高识别率,提出了采用AdaBoost算法提取脸部整体特征,主动外观模型提取局部特征(脸部几何特征),融合局部与整体特征后用支持向量机进行分类的方法。本文在一个由AR、FERET、CAS-PEAL、网上收集和实验室自行采集所共同组成的,包含21,300余张人脸的数据库上,进行了大量有意义的实验。实验结果显示,融合了整体特征和局部特征后,识别率比基于(单独)整体特征的、基于(单独)局部特征的有很大的提高,达到了90%以上。实验结果还表明,识别过程中由于有了几何特征的参与,对于脸部光照、姿态等变化表现出了更强的鲁棒性和更好的识别结果。
大多数性别分类方法是对整幅人脸图像提取特征,相对的,各脸部子区域受脸部表情变化的影响更小。为提高脸部表情变化时的性别识别率,提出一种基于子区域特征的性别识别方法。本文详细比较了脸部各子区域对性别识别的贡献大小,包括眼睛、鼻子、嘴巴、下巴、左眼区域,脸内部区域(包括眼睛、鼻子、嘴巴和下巴)、以及整个人脸区域(带头发的人脸区域)。用FERET和CAS-PEAL两个人脸数据库进行实验,实验结果表明人脸部各子区域同样具有足够的与性别相关的信息,用单个子区域进行性别识别的正确率可以达到80%以上。通过融合脸部对性别识别贡献较大的子区域,利用各子区域包含的脸部特征信息是互补的,进一步提高了性别识别准确率。
提出利用能互相补充的脸部信息,即脸部灰度图像、脸部Gabor小波特征、以及眼睛区域,来提高年龄估计的准确率。用Gabor小波提取脸部特征的方法,Gabor小波具有多方向和多尺度选择的特性,并且能够获取对应于空间和频率的局部结构信息,对于图像的亮度和对比度变化以及人脸姿态变化具有较强的鲁棒性。为进一步提高准确率,针对男性和女性在人脸年龄特征表现上的差异,以性别信息作为年龄估计的先验知识。用支持向量机对各脸部特征分别进行年龄估计,融合后得到年龄识别结果,并对融合方法进行了改进。
Recently, Biometrics has been applied into pattern recognition including face recognition, fingerprint recognition, iris recognition, palm print recognition etc. Compared with other biometric personal authentication technologies, face recognition technology is natural, convenient and non-contact. These advantages make it be applied widely in security surveillance, verification, man-machine conversation and so on. Gender classification and age estimation is an attempt to give the computers the ability to discriminate the gender information and estimation age from a face image. Gender classification and age estimation has become the hotspot of computer vision and pattern recognition for its important application prospects in identity recognition, man-machine interface, video index and robot vision and so on. However, it is still one of the most challenging problems in the fields of computer vision.
Since feature extraction algorithms used by gender classification and age estimation just need to extract the features of face area, face detection is a necessary step in automatic gender classification and age estimation systems. Moreover, the accuracy of face detection would affect the effectivity of feature extraction. So multi-view face detection method based on Adaboost learning algorithm is applied, and its cascaded classifier structure and pyramid architecture both improve the speed of detection. The experimental results show the face detection method is rapid and effective.
In order to normalize the size of face image, an eye location method based on Adaboost and fast radial symmetry transform is proposed. Firstly, fast radial symmetry transform can search feature points rapidly. Secondly, an eyebrow region detection algorithm based on Adaboost is presented to reduce the range of searching eyes, which can decrease the influence of other feature points on eye location. Finally, the precise location of eyes can be obtained by using pupil model and geometrical features of eyes. Furthermore, the results of eye location are used to initialize active appearance model, which is applied to locate facial feature points so as to extract the local features of face images.
Gender classification and face recognition both need to find effective and stable facial features, but ultimately identify the different patterns. In this paper, we compared those methods which used in gender classification, including local binary pattern, neural network, Adaboost, support vector machines (SVM), and image pixels as input. We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with different combinations of automatic face detection, face alignment and gender classification, and show the classification rates for different face images sizes.
Based on the type of features used, previous studies can be broadly classified into two categories: appearance feature-based (global) and geometric feature-based (local). The former finds the decision boundary directly from training images while the latter is based on geometric features such as eyebrows thickness, nose width, etc. The local and global features supplement each other under some conditions. In this paper, a novel gender classification method based on frontal face images is presented. In this work, the global features are extracted using AdaBoost algorithm. Active Appearance Model locates 83 landmarks, from which the local features are characterized. After the fusion of the local and global features, the mixed features are used to train support vector machine classifiers. This method is evaluated by the recognition rates over a mixed face database containing over 21,300 images from 4 sources (AR, FERET, CAS-PEAL, WWW and a database collected by the lab). Experimental results show that the hybrid method outperforms the unmixed appearance- or geometry-feature based methods and achieve a classification rate over 90%.
In the past, most computational models of gender classification use global information (the whole face image) giving equal weight to all areas of the face, irrespective of the importance of internal features. Intuitively, we argue that smaller facial regions, if judiciously selected, would be less sensitive to expression variations and may lead to better overall performance. We evaluate the significance of different facial regions for gender perception. Our work on gender classification is one of the first attempts to report a detailed evaluation of the significance of different facial regions including the whole face (including hairline), the internal face, the upper region of face, the lower region of face, the left eye, the nose, and the mouth. Considering the significance of facial regions, we propose a fusion-based method, combining the classification results of three facial regions, for improving the robustness to facial expressions.
We notice that most computational models of the existing age estimation methods consider only the entire face as a global feature, they do not take into account just what other regions of the face as local features. We propose a novel method for age estimation that combines information from multiple facial features for improving accuracy and robustness. The facial features that we consider are the grayscale image of the face, the Gabor wavelet representation of the face, and the eyes. The Gabor wavelet representations are robust for illumination and expressional variability, so are used widely in facial feature modeling in recent years. The eyes are essentially unaffected by beards and mustaches and quite robust to facial expressions and occlusions. Moreover, the area around the eyes was found to be the most significant for the task of age estimation. The idea is to use complementary information for improving overall performance. Further, we propose a fusion method that further improves the accuracy. Each feature provides an opinion on the claim in terms of a confidence value which is calculated by SVMs. The confidence values of all the three features are then fused for final age estimation. The proposed fusion method works quite well and yields a significant improvement in age estimation over that achievable with any single feature.
引文
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