Face Detection And Recognition With Variant Illumination

Human face detection and recognition is an important research subject in the field of pattern recognition and computer vision. It has been widely used in many applications, such as public security, smart surveillance, video conference, multimedia and digital entertainment. After over 30 years of development, this technology has been developed steadily in controlled conditions and gains the better performance in an optimal situation. However, when it comes to uncontrolled conditions, such as different illumination conditions, pose variations, mixture of emotions and object shelter, the accuracy of face detection and recognition will dramatically decline. Therefore, the research faces enormous challenges in real-world applications.This paper does a lot of research on the training of the face detector, the face feature extraction, the design of the classifier, etc. In particular, this paper focuses on the problem of face detection and recognition in complex illumination environment and describes the work on face image enhancement and the obtaining of the invariant face features under illumination. The major contributions of this paper are as follows:First, in order to reduce the high false face detection rate of the Adaboost algorithm, the algorithm of Adaboost-SVM is presented. The Adaboost algorithm uses the Haar wavelet and the integral image and cascading to realize the real time face detection, its false face detection rate is high under complex background. In this paper, a face detector using the Adaboost algorithm(FD_Adaboost) is trained firstly and is used to detect faces in the face database,then, the face detection result is manual classified to two class:positive class and negative class,after that,the two-class training samples is used to train the face detector based on SVM(FD_SVM). Finally, FD_Adaboost and FD_SVM are cascaded to constitute the Adaboost-SVM face detector. Experiment results show that the Adaboost-SVM algorithm achieves good results on face detetion and obviously reduces the false face detection rate, the number of false detection window can be mostly reduced by 82.91%.Second, studies show that the performance of face detection will be obviously reduced under the complex illumination environment. In order to solve this problem, this paper presents a fast self-adaption image enhancement algorithm. To deal with the color distortion of enhanced image and reduce the computational complexity, the paper proposes an improved multi-scale Retinex algorithm. Meanwhile, the paper presents a fast self-adaption image enhancement algorithm that combines logarithmic transformation with nonlinear transformation. Compared with histogram equalization(HE), single-scale Retinex(SSR) and multi-scale Retinex(MSR), the self-adaption image enhancement algorithm increases the face detection rate and reduces the false face detection rate obviously..Third, in order to solve the problem of the high dimensionality of Gabor wavelet facial feature, the paper reduces the feature dimension by using two-dimensional linear subspace and classifies the face images using SVM. Firstly, the face image is processed by multi-scale and multi-directional Gabor wavelet to produce the Gabor EigenFace.Then, the feature dimension of the Gabor EigenFace is reduced by using two-dimensional linear subspace, finally, SVM is used to classify the face images. Experiment results show that this method not only acquires the facial feature effectively, but also solves the dimensionality curse caused by Gabor wavelet. The classification result is perfect.Fourth, this paper gives a review on face recognition algorithm under complex illumination environment and proposes a new morphological wavelet quotient image algorithm. The facial feature independent of illumination is contributed to improve the face recognition rate in complex illumination environment. In order to get the above facial feature, a morphologic closing computation operator is applied to process the face image, then, filters the high frequency components in wavelet domain. The result image(RI) is the lighting estimation of the source face image(SI).when RI and SI divide each other, the quotient image is independent of illumination. Compared with single scale Retinex and morphological quotient image (MQI), the new method has better performance on keeping face recognition feature under complex illumination environment and achieves a high recognition rate.In conclusion, This paper discusses the strategy of reducing the false face recognition rate, the face feature extraction and recognition and the improvement of the face detection and recognition under complex illumination environment, moreover, it puts forward specific proposals and experiment results which contributes to the research of the face detection and recognition and the practical application...