人脸检测方法研究
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摘要
人脸检测问题最初作为自动人脸识别系统的定位环节被提出，近年来由于其在安全访问控制、视觉检测、基于内容的检索和新一代人机界面等领域的应用价值，开始作为一个独立的课题受到研究者的普遍重视。到目前为止，人脸检测问题由于自身的复杂性，仍旧未能得到彻底解决。最新的进展是Viola等人的基于积分图像的Adaboost方法，其层叠分类器在人脸检测方面速度快且性能与Rowley的ANN方法基本相同，在此基础上所构建的检测系统是第一个真正实时的人脸检测系统。但是作为一种新兴技术，Adaboost方法在人脸检测领域的研究还有待探索和完善。例如：弱分类器的选择、训练算法的完善、多尺度检测计算量大、缺乏和先验知识的整合能力等等。本文作者结合研究生阶段所参加的科研项目，对人脸检测问题作了一些探讨，对基于积分图像的Adaboost人脸检测方法存在的部分问题分别进行了研究，并给出了相应的解决方案。本文工作包括：
(1)Adaboost学习算法的改进
基于积分图像的Adaboost方法采用一种称为“积分图像”的图像表示方法，快速计算出弱分类器用到的特征。然后基于Adaboost学习算法，从一个较大的特征集中选择少量的关键的视觉特征，产生一个高效的强分类器。再用级联的方式将单个的强分类器合成为一个更加复杂的层叠分类器，使图像的背景区域快速地丢弃，而在有可能存在目标(人脸)的区域花费更多的计算，其在人脸检测方面速度相当快。本文在对Adaboost学习特点的深入分析基础上，在构成弱分类器用到的特征选取方面作了一定改进，提高了分类器的性能，并提出一种有效的方法来完善学习算法。实验结果表明，新的人脸检测器检测效果有所提高，构造效率则显著提高。
(2)基于肤色的人脸检测方法研究
本文选择目前广泛使用的YCbCr色彩模型来进行肤色分割和人脸验证，最终构建了一个纯粹利用肤色来检测人脸的检测系统。本文采用了一种光照校正算法部分消除光照对颜色影响，提高了检测算法的适应能力，并对肤色区域分割和肤色区域合并算法进行了一定研究。实验结果表明，这种人脸检测方法对于不同姿态、不同表情的人脸均有较好适用性。
(3)提出一种基于肤色区域分割预处理与多层叠分类器验证相结合的人脸检测方法
基于积分图像的Adaboost方法构造的多层叠分类器用于人脸检测虽然取得了极大成功，但同样因为要对输入图像穷举扫描，在很大程度上影响了检测效率，这限制了它在一些实时性要求的较高的大幅图像人脸检测应用中的使用。本文在多层叠分类
硕士论文
人脸检测方法研究
器基础上，通过肤色区域分割预处理产生候选人脸区域。并在候选区域上采用多层叠
分类器通过受限窗口扫描进行人脸检测，由此减少传统多尺度穷举扫描检测方法的计
算代价。而且通过候选区域扫描限制了人脸误识情况的发生，从而提高系统的总体性
能。实验结果表明，新的人脸检测器计算速度有很大提高(大幅彩色图像中)，同时
保持了多层叠分类器人脸检测的稳定性。
Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, all of these researching directions involve in one problem-face detection and location. In other words, before this face processing, we must know faces' locations and scales. Consequently, to build an automated face processing system which analyzes the information contained in face images, robust and efficient face detection algorithms are required. The research on face detection has lasted for more than twenty years. But, up to now, due to the complexity of the purpose such as the diversity of face patterns, variable lighting condition and so on, many researches can not resolve the problem completely even if they have studied it for long time, hi this thesis, the author has done some work on the face detection.
The work includes:
(1) The improvement of AdaBoost learning algorithm
Using a training set of positive and negative image, Paul Viola has used AdaBoost to build a robust scale-invariant image classifier for face detection. Based on the deeply analysis of AdaBoost learning, this paper improves the classical training methods in two ways: Firstly, the basic and over-complete set of feature is extended by an set of rotated features ,which add additional domain-knowledge to the learning framework and which is otherwise hard to learn. Secondly, this paper derives a new method to optimize the learning procedure, which compute the features only twice and use more less space in memory.
(2)Face detection based on skin color
Among many color spaces, this paper used YCbCr components. Since in the YCbCr color space, the luminance information is contained in Y component; and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. This paper proposes a face detection algorithm for color images in the presence of varying lighting conditions as well complex backgrounds. Based on a novel lighting compensation, our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye and mouth maps for verifying each face candidate. Experimental results demonstrate successful face detection over a range of facial variations in color, position, scale and expression in color images.
(3) A face detector based on skin color segmenting pre-processing and multiple cascade of classifiers based on AdaBoost validation.
Multiple cascades of classifiers based on AdaBoost statistical learning method have been applied successfully in face detection problem. The advantage of using multiple cascades for face detection is the feasibility of training a system to capture the complex class conditional density of face patterns. However, one drawback is that it need apply an exhaustive window scanning technique to input image for possible face locations at all scales. This reduces the executing efficiency of the detector in great extent and restricts it to apply to some real-time face detection application. The results of experiments show the new face detector achieves a higher executing efficiency and fewer false detect than traditional multiple cascades detector.
(1)Adaboost学习算法的改进
基于积分图像的Adaboost方法采用一种称为“积分图像”的图像表示方法，快速计算出弱分类器用到的特征。然后基于Adaboost学习算法，从一个较大的特征集中选择少量的关键的视觉特征，产生一个高效的强分类器。再用级联的方式将单个的强分类器合成为一个更加复杂的层叠分类器，使图像的背景区域快速地丢弃，而在有可能存在目标(人脸)的区域花费更多的计算，其在人脸检测方面速度相当快。本文在对Adaboost学习特点的深入分析基础上，在构成弱分类器用到的特征选取方面作了一定改进，提高了分类器的性能，并提出一种有效的方法来完善学习算法。实验结果表明，新的人脸检测器检测效果有所提高，构造效率则显著提高。
(2)基于肤色的人脸检测方法研究
本文选择目前广泛使用的YCbCr色彩模型来进行肤色分割和人脸验证，最终构建了一个纯粹利用肤色来检测人脸的检测系统。本文采用了一种光照校正算法部分消除光照对颜色影响，提高了检测算法的适应能力，并对肤色区域分割和肤色区域合并算法进行了一定研究。实验结果表明，这种人脸检测方法对于不同姿态、不同表情的人脸均有较好适用性。
(3)提出一种基于肤色区域分割预处理与多层叠分类器验证相结合的人脸检测方法
基于积分图像的Adaboost方法构造的多层叠分类器用于人脸检测虽然取得了极大成功，但同样因为要对输入图像穷举扫描，在很大程度上影响了检测效率，这限制了它在一些实时性要求的较高的大幅图像人脸检测应用中的使用。本文在多层叠分类
硕士论文
人脸检测方法研究
器基础上，通过肤色区域分割预处理产生候选人脸区域。并在候选区域上采用多层叠
分类器通过受限窗口扫描进行人脸检测，由此减少传统多尺度穷举扫描检测方法的计
算代价。而且通过候选区域扫描限制了人脸误识情况的发生，从而提高系统的总体性
能。实验结果表明，新的人脸检测器计算速度有很大提高(大幅彩色图像中)，同时
保持了多层叠分类器人脸检测的稳定性。
Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, all of these researching directions involve in one problem-face detection and location. In other words, before this face processing, we must know faces' locations and scales. Consequently, to build an automated face processing system which analyzes the information contained in face images, robust and efficient face detection algorithms are required. The research on face detection has lasted for more than twenty years. But, up to now, due to the complexity of the purpose such as the diversity of face patterns, variable lighting condition and so on, many researches can not resolve the problem completely even if they have studied it for long time, hi this thesis, the author has done some work on the face detection.
The work includes:
(1) The improvement of AdaBoost learning algorithm
Using a training set of positive and negative image, Paul Viola has used AdaBoost to build a robust scale-invariant image classifier for face detection. Based on the deeply analysis of AdaBoost learning, this paper improves the classical training methods in two ways: Firstly, the basic and over-complete set of feature is extended by an set of rotated features ,which add additional domain-knowledge to the learning framework and which is otherwise hard to learn. Secondly, this paper derives a new method to optimize the learning procedure, which compute the features only twice and use more less space in memory.
(2)Face detection based on skin color
Among many color spaces, this paper used YCbCr components. Since in the YCbCr color space, the luminance information is contained in Y component; and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. This paper proposes a face detection algorithm for color images in the presence of varying lighting conditions as well complex backgrounds. Based on a novel lighting compensation, our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye and mouth maps for verifying each face candidate. Experimental results demonstrate successful face detection over a range of facial variations in color, position, scale and expression in color images.
(3) A face detector based on skin color segmenting pre-processing and multiple cascade of classifiers based on AdaBoost validation.
Multiple cascades of classifiers based on AdaBoost statistical learning method have been applied successfully in face detection problem. The advantage of using multiple cascades for face detection is the feasibility of training a system to capture the complex class conditional density of face patterns. However, one drawback is that it need apply an exhaustive window scanning technique to input image for possible face locations at all scales. This reduces the executing efficiency of the detector in great extent and restricts it to apply to some real-time face detection application. The results of experiments show the new face detector achieves a higher executing efficiency and fewer false detect than traditional multiple cascades detector.
引文
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[2] 艾海舟，梁路宏等，基于肤色和模板的人脸检测，软件学报，Vol．12，No．12 2001
[3] M.Propp and A.Samal. Artificial neural network architecture for human face detection. Intelligent Engineering Systems Through Artificial Neural Networks, 2,1992
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