Face Detection and Recognition Using Skin Color and AdaBoost Algorithm Combined with Gabor Features and SVM Classifier
A general statement of face recognition problem (in computer vision) can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a database of stored faces. For the fully automatic system of face recognition, a lot of researches have been done in many directions that eliminate the difficulties of finding and identifying faces with the illumination and pose problems. However, most of them just concentrate on these issues separately instead of consider the efficiency of the whole system while both method in face detection and recognition work together. This research tried to make compensation between those two main components. In the recognition step, eigenface technique was used to normalize faces in the database. At first, we calculate the face covariance matrix and the associated face eigenvectors. These will be the face prints. Different vectors of weights can represent different faces. After that, any face can be reconstructed from a set of weights. So we can recognize a new picture of a familiar face. For the face detection process, skin-color model was used to separate skin areas from the background in order to cut out the face block for recognizing. The whole process of face recognition can be separate into two phases: Training phase and Recognition phase. At first, we train the recognition system by using a sequence of images for each person to create the alternative face print. In the second phase, the result of the first phase will be used to match with the input face image in the video stream and then show the conclusion on whether people in video is granted or not. The experiments included both aspects: independent component and holistic system on the popular databases of face detection, recognition in still image and video. The result achieved a recognition rate at from 80 percents to 100 percents with a small number of 10 or 24 training samples.