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Abstract-This paper presents the University of Southampton Multi-Biometric Tunnel, a constrained environment that is designed with airports and other high throughput environments in mind. It is able to acquire a variety of non-contact biometrics in a non-intrusive manner. The system uses eight synchronised IEEE1394 cameras to capture gait and additional cameras to capture images from the face and one ear, as an individual walks through the tunnel. We demonstrate that it is possible to achieve a 99.6% correct classification rate and a 4.3% equal error rate without feature selection using the gait data collected from the system; comparing well with state-of-art approaches. The tunnel acquires data automatically as a subject walks through it and is designed for the collection of very large gait datasets.

| Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. The current state of the art can achieve over 90% identification rate under situations where the training and test data are captured under similar conditions, while recognition rates with change of clothing, shoe, surface, illumination, and pose usually decrease performance and are the subject of much of the current study.Recognition can be achieved on outdoor data with uncontrolled illumination and at a distance when other biometrics could not be used. We shall show how this position has been achieved, covering most approaches to recognition by gait and the databases on which performance has been evaluated. We shall describe the context of these approaches, show how recognition by gait can be achieved and how current limits on performance are understood. We shall describe results on the most popular database, showing how recognition can handle some of the covariates that can affect recognition. We shall also investigate the supporting literature for this research, since the notion that people can be recognized by gait has support not only in medicine and biomedicine, and also in literature and psychology and other areas. In this way, we shall show that this new biometric has capability and research and application potential in other domains.

Abstract-We present a new method for front-view gait biometrics which uses a single non-calibrated camera and extracts unique signatures from descriptors of a silhouette's deformation. The proposed approach is particularly suitable for identification by gait in the real world, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and where the CCTV images usually present subjects from an upper front-view. Tests on three different gait databases with subjects walking towards the camera have been performed. The obtained results, with mean CCR of 96.3%, show that gait recognition of individuals observed the front can be achieved without any knowledge of camera parameters. Moreover, the method has been applied to three different walking directions and the results have been compared with the algorithms found in literature. The performance of the proposed system is particularly encouraging for its appliance in surveillance scenarios.

The objective in defining feature space is to reduce the dimension of the original pattem space yet maintaining discriminatory power for classijkation [l]. To meet this objective in the context of ear and face biometrics a novel force field transformation has been developed in which the image is treated as an array of Gaussian attractors that act as the source of a force field. The directional properties of the force field are exploited to automatically locate a small number of potential energy wells and channels that form the basis of a characteristic feature vector. Here, we generalise the analysis, and the stock of applications.