After comparing and analyzing existing face detection methods based on complexion, a skin model used to detect human faces in this thesis, Gaussian distribution model is proposed.
研究了常见的基于肤色的人脸检测算法并分析比较其优缺点，给出本文利用肤色特征进行肤色检测时所采用的肤色模型——高斯分布模型。
This method can detect human face quickly under the complex background, and is not affected by the rotating Angle of a face.
该方法能在复杂背景下快速检测人脸，并且不受人脸旋转角度的影响。
It is also a practical method to detect the motion human face in digital videos combined with the motion information, gray level information and skin color information.
同时，也是一种充分利用了视频图像运动信息和人脸肤色信息进行运动人脸检测的实用方法。
Experiments show that these methods can detect human face from complex background and can segment it efficiently.
试验结果表明，该方法能较好地从复杂背景中检测出人脸区域并能较完整地分割出人脸。
The eigen skin-color method is for the color images. For gray images, this paper USES face recognition method based on hidden Markov models to detect human faces.
特征肤色的方法是针对彩色图像的人脸检测方法，针对灰度图像本文采用基于隐马尔可夫模型的人脸识别方法来检测人脸。
A new method which combined face profile, ear feature and relative position of ear and face profile to detect and recognize human ear is proposed in this paper.
为实现人耳的自动检测与识别，提出了结合侧面轮廓特征、人耳统计特征以及人耳与侧面轮廓的位置特征进行人耳检测与识别的方法。
The Feature invariant approaches can detect the human faces fast, but only some kind of face can be detected.
基于特征不变量的方法，实现速度比较快，但都只适用于某一类图像。
With a certain interval of time to detect the presence or absence of face and the availability of the human eye, to judge learner status, and implement appropriate measures.
采用一定间隔时间来检测人脸的有无与人眼的有无，据此判断学习者状态，并实施相应措施。
The main purpose of this paper is to detect the human face with a rapid and accurate method in a given image.
本文研究的主要目的是对于一幅给定的图像，快速而准确地检测出人脸。
The main purpose of this paper is to detect the human face with a rapid and accurate method in a given image.
本文研究的主要目的是对于一幅给定的图像，快速而准确地检测出人脸。
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