We present an e cient framework for the detection and tracking of human faces with an active camera. The Bhattacharyya coe cient is employed as a similarity measure between the color distribution of the face model and face candidates. The proper derivation of these distributions allows the use of the spatial gradient of the Bhattacharyya coe cient to guide a fast search for the best face candid...

Our objective is to track multiple faces in a video sequence. We combine a frame-based face detection algorithm based on a coarse-to-fine search with a dynamic Markov model for the joint evolution of poses and observations. The link between the two is the trace model, a probability distribution on the global history of online processing during static face detection. The trace is our frame-based...

As the popularity of digital cameras increases, some limitations of digital technology are becoming apparent and on the other hand it has become imperative to ensure security of information. Looking ahead towards the authentication scheme with a view to optimize it. Skin detection [1] is a very popular and useful technique for detecting and tracking human-body parts. It receives much attention ...

Face detection is the technique to locate various faces in an image, so that the face region will be extracted from the background. Face detection is considered as the backbone of topics like face recognition, face tracking, expression recognition etc. as if faces could be located exactly in any scene; recognition process would not be too much difficult. Recognition is mainly used for the purpo...

We introduce a stochastic model to characterize the online computational process of an object recognition system based on a hierarchy of classifiers. The model is a graphical network for the conditional distribution, under both object and background hypotheses, of the classifiers which are executed during a coarse-to-fine search. A likelihood is then assigned to each history or “trace” of proce...

We present a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user’s head under large rotation and illumination variations. Precision and usability of ou...

In this paper algorithm to detects human facial features like the mouth, nose and eyes in a full frontal face image is proposed. The new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their corresponding geometrical point. This project presents and implements a facial feature point extraction and recognition method of facial expression an...

Human tracking is an important function for an automatic surveillance system using a vision sensor. However, it is difficult to identify a human exactly in an image due to the variety of poses. This paper describes a method for automatic human tracking based on face detection using Haar-like features and mean-shift tracking. The method increases its trackability by using multi-viewpoint images....

Face detection is a fundamental research area in computer vision field. Most of the face-related applications such as face recognition and face tracking assume that the face region is perfectly detected. To adopt a certain face detection algorithm in these applications, evaluation of its performance is needed. Unfortunately, it is difficult to evaluate the performance of face detection algorith...

The colors of the human skin represent a special category of colors, because they are distinctive from the colors of other natural objects. This category is found as a cluster in color spaces, and the skin color variations between people are mostly due to differences in the intensity. Besides, the face detection based on skin color detection is a faster method as compared to other techniques. I...