Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

A new method for real-time tracking of non-rigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real-time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.

A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking. Keywords: non-rigid object tracking; target localization and representation; spatially-smooth similarity function; Bhattacharyya coefficient; face tracking. 1

A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people and groups. A novel, adaptive background subtraction method that combines color and gradient information is used to cope with shadows and unreliable color cues. People are tracked through mutual occlusions as they form groups and separate from one another. Strong use is made of color information to disambiguate occlusions and to provide qualitative estimates of depth ordering and position during occlusion. Simple interactions with objects can also be detected. The system is tested using both indoor and outdoor sequences. It is robust and should provide a useful mechanism for bootstrapping and reinitialization of tracking using more specific but less robust human models. Key Words: background subtraction, groups of people, human activity, tracking 1.

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach.

In this paper we address the problem of segmenting foreground regions corresponding to a group of people given models of their appearance that were initialized before occlusion. We present a general framework that uses maximum likelihood estimation to estimate the best arrangement for people in terms of 2D translation that yields a segmentation for the foreground region. Given the segmentation result we conduct occlusion reasoning to recover relative depth information and we show how to utilize this depth information in the same segmentation framework. We also present a more practical solution for the segmentation problem that is online to avoid searching an exponential space of hypothesis. The person model is based on segmenting the body into regions in order to spatially localize the color features corresponding to the way people are dressed. Modeling these regions involves modeling their appearance (color distributions) as well as their spatial distribution with respect to the body. We use a non-parametric approach based on kernel density estimation to represent the color distribution of each region and therefore we do not restrict the clothing to be of uniform color. Instead, it can be any mixture of colors and/or patterns. We also present a method to automatically initialize these models and learn them before the occlusion. 1

In this paper a vehicle tracking algorithm is presented based on the combination of a per pixel background model (an extension of work by Stauffer and Grimson [12]) and a set of single hypothesis foreground models based on a general model of object size, position, velocity, and colour distribution. Each pixel in the scene is thus `explained' as either background, belonging to one of the foreground objects or as noise. Calibrated ground-plane information is used within the foreground model to strengthen the object size and velocity consistency assumptions.

An integrated system for the acquisition, normalisation and recognition of moving faces in dynamic scenes is introduced. Four face recognition tasks are defined and it is argued that modelling person-specific probability densities in a generic face space using mixture models provides a technique applicable to all four tasks. The use of Gaussian colour mixtures for face detection and tracking is also described. Results are presented using data from the integrated system. Key words: Face recognition, Biometrics, Gaussian mixtures, Colour models. 1 Introduction Face recognition in general and the recognition of moving people in natural scenes in particular, require a set of visual tasks to be performed robustly. These include (1) Acquisition: the detection and tracking of face-like image patches in a dynamic scene, (2) Normalisation: the segmentation, alignment and normalisation of the face images, and (3) Recognition: the representation and modelling of face images as identities, and ...

We present an efficient framework for the detection and tracking of human faces with an active camera. The Bhattacharyya coefficient 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 coefficient to guide a fast search for the best face candidate. The optimization, which is based on mean shift analysis, requires only a few iterations to converge. Scale changes of the trackedfaceare handled by exploiting the scale invariance of the similarity measure and the luminance gradient computed on the border of the hypothesized face region. The detection and tracking modules are almost identical, the difference being that the detection involves mean shift optimization with multiple initializations. Our dual-mode implementation of the camer controller determines the pan, tilt, and zoom camera to switch between smooth pursuit and saccadic movements, as a function of the target presence in the fovea region. The resulting system runs in real-time on a standard PC, being robust to partial occlusion, clutter, facescale variations, rotations in depth, and fast changes in subject/camera position.

A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people and groups. A novel, adaptive background subtraction method that combines colour and gradient information is used to cope with shadows and unreliable colour cues. People are tracked through mutual occlusions as they form groups and part from one another. Strong use is made of colour information to disambiguate occlusions and to provide qualitative estimates of depth ordering and position during occlusion. Some simple interactions with objects can also be detected. The system is tested using indoor and outdoor sequences. It is robust and should provide a useful mechanism for boot-strapping and reinitialisation of tracking using more specific but less robust human models.