In this paper, a biometric authentication system based on measurements of the user's three-dimensional (3-D) hand geometry is proposed. The system relies on a novel real-time and low-cost 3-D sensor that generates a dense range image of the scene. By exploiting 3-D information we are able to limit the constraints usually posed on the environment and the placement of the hand, and this greatly contributes to the unobtrusiveness of the system. Efficient, close to real-time algorithms for hand segmentation, localization and 3-D feature measurement are described and tested on an image database simulating a variety of working conditions. The performance of the system is shown to be similar to state-of-the-art hand geometry authentication techniques but without sacrificing the convenience of the user.

This work presents an information fusion mechanism for image segmentation using multiple cues. Initially, a fuzzy clustering of each cue space is performed and corresponding membership functions are produced on the image coordinates space. The latter include complementary as well as redundant information. A fuzzy inference mechanism is developed, which exploits these characteristics and fuses the membership functions. The produced aggregate membership functions represent objects, which bear combinations of the properties specified by the cues. The segmented image results after post-processing and defuzzification, which involves majority voting. A fuzzy rule based merging algorithm is finally proposed for reducing possible oversegmentation. Experimental results have been included to illustrate the steps and the efficiency of the algorithm.

This paper addresses the reconstruction of 3D human body models from 2D video sequences. Considering that the input frames are already segmented, the proposed technique consists of three stages. These stages are independently applied over each segmented frame. Firstly, a skeleton of a human figure obtained from the segmented image is extracted by means of a fast algorithm based on a Voronoi diagram of the boundary points. Afterwards, the skeleton is labelled according to the human body parts (e.g. head, upper arm, lower arm, torso, etc). Secondly, an initial 3D model posture is estimated from the labelled skeleton. Finally, an iterative closest point (ICP) implementation is used to refine the initial model posture by maximizing the similarity between the projected 3D model and the segmented image. Experimental results with video sequences are presented.