Color Image Face Detection Based On Pattern Recognition And Fuzzy Theory

Recent years, research on face detection has been very hot. This is because of its both academic and commercial value. The broken advancement of this important subject will bring great push to the fields such as face recognition, expression and pose recognition, video surveillance systems and so forth. And thus promotes the development of computer vision, pattern recognition and other computer science branch and even the development of whole computer science.Currently research on face detection has gone through deeply. What is worth mention is that by so far, most relative research on facial feature extraction both home and abroad has focused on eyes, noses and mouth features and research on eyebrows is so less. In fact, human eyebrow has enough good stability, anti-interference and diversity. Their shape wont't vary much like eyes and mouth so they can be a good independent biologic features to be used in face detection.This thesis focuses on the research of human face detection realized by eyebrows detection in color images. As is a critical step towards face detection and recognition, the positions of eyebrows are commonly used for estimating a human face's scale and orientation. The results of eyebrows localization premise the verification of eyebrow region in this thesis. Biomimetic pattern recognition method is used in the validation of the eyebrows and face for its excellent performance of recognition. And the samples which are rejected in previous recognition step will be recognized again by fuzzy pattern recognition method. In particular, main contributions of this thesis detail are as follows:Firstly, based on the prior knowledge that two eyebrows are in the face, i.e. eyebrows are surrounded by skin, skin segmentation can be applied to reduce the search region. Here, a method of segmenting the skin likelihood grey scale image is proposed based on the optimum threshold technique. Compared with those using a fixed threshold value, it's more effective. Morphology filter and euler number principle is further used on the previously segmented skin likelihood region to exclude non-face region.Secondly, an idea of realizing face detection through eyebrows detection is proposed and a research based on it is done. Through an experiment of evaluating the role that eyebrows play in human face detection which results in that eyebrows are more important than eyes to some extent, we benefit from it that our research has experimental foundation and it's meaningful. To reduce the amount of biomimetic pattern verification, the algorithm utilizes luminance and gray scale projection information of eyebrows to further determine candidate regions. For those error-detected regions, geometry information of eyebrows, such as the angle of eyebrow pair and the ratio between the distance of two eyebrows and the width of the outer rectangle of detected region, can be the rules of removal.Thirdly, if we utilize the whole face image data to train, the detection procedure will be very time-consuming because of its high space dimension. So in this thesis we first collect eyebrows images, normalize them by scalar and then extract moment invariants to form the eigenvector for training. In the actual test procedure, the eyebrows region detected by previous algorithm is normalized by scalar and then 20 orders'Legendre moments are calculated, which forms the eigenvector for testing. By doing so, the detection procedure will take far less time than using the whole face data to form the eigenvector.Fourthly, we use biomimetic pattern recognition method to confirm the candidate eyebrow region based on modeling of the training samples. Those samples which are rejected to be recognized in this turn will be further determined by fuzzy pattern recognition. That means a proper degree function of membership in a category to be constructed and then to determine a threshold through an amount of experiment. In this way the final confirmation of the eyebrows will be give out.Experimental results show that the method of this thesis is effective. It's useful both theoretically and practically.