CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor刘迎建
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract统计表明,家庭照片中75%的人脸是以侧脸的姿态出现的,因此处理侧脸的能力在许多有关人脸的应用中显得尤其重要.多角度人脸检测仍然是一个具有挑战性的工作.这种挑战性来源于人脸自身的外貌,光照情况和表情有很大的变化.而人脸姿态带来的变化使得这一问题变得更为困难,因为多角度的人脸在图像空间中比起正脸要复杂得多. 为了克服序贯搜索算法AdaBoost的单调性问题,我们提出了一种新的Boosting算法,FloatBoost.AdaBoost是一种前向的搜索过程,它是一种贪心算法.这种序贯搜索的假设前提是单调性,也就是说任意加入一个新的特征,准则函数的值都不会下降.当单调性这一前提被破坏之后,AdaBoost的搜索过程往往就不是最优的.FloatBoost利用Floating搜索的思想解决了AdaBoost具有的非单调性问题. 我们采用FloatBoost算法构造了一个用于多角度人脸检测的系统.这一系统使用一种由粗到细,由简单到复杂的金家塔式的检测器结构.FloatBoost用于训练此结构中的每一个构件,用更少的特征取得了与AdaBoost相近甚至更好的检测性能.这一工作是目前第一个提出的实时的多角度人脸检测系统.在奔腾Ⅲ700MHz的机器上,每秒可以处理5桢320*240大小的图像.
Other AbstractDealing with multi-view faces is important for many face-related applications. Statistics show that approximately 75% of the faces in home photos are non-frontal. Multi-view face detection has been a challenging problem. The challenge is firstly due to large amount of variation and complexity brought about by the changes facial appearance, lighting and expression. Changes in facial view (pose) further complicate the situation because the distribution of multi-view faces in a feature space is more dispersed and more complicated than that of frontal faces. A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost is a sequential forward search procedure using the greedy selection strategy. The premise offered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search into AdaBoost to solve the non-monotonicity problem encountered in the We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classification accuracy that AdaBoost withy a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320*240 pixels on a Pentimu-Ⅲ CPU of 700 MHz.
Other Identifier645
Document Type学位论文
Recommended Citation
GB/T 7714
张震球. 多角度人脸检测的统计学习研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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