There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective fac...

Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These...

in this paper, we propose a novel method for fully automatic detection and tracking of human heads and faces in video sequences. the proposed algorithm consists of two modules: a face detection module and a face tracking module. the detection module, detects the face region and approximates it with an ellipse at the first frame using a modified version of adaboost cascaded classifier. the detec...

Viola and Jones [1] introduced a new and effective face detection algorithm based on simple features trained by the AdaBoost Algorithm, Integral Images and Cascaded Feature sets. This paper attempts to replicate their results. The Feret Face data set is used as the training set. The AdaBoost Algorithm, simple feature set and Integral Images are briefly explained and implemented in our Matlab ba...