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Abstract Wireless sensor networks allow detailed sensing of otherwise unknown and inaccessible environments. While it would be beneficial to include cameras in a wireless sensor network because images are so rich in information, the power cost of transmitting an image across the wireless network can dramatically shorten the lifespan of the sensor nodes. This paper describe a new paradigm for the incorporation of imaging into wireless networks. Rather than focusing on transmitting images across the network, we show how an image can be processed locally for key features using simple detectors. Contrasted with traditional event detection systems that trigger an image capture, this enables a new class of sensors which uses a low power imaging sensor to detect a variety of visual cues. Sharing these features among relevant nodes cues specific actions to better provide information about the environment. We report on various existing techniques developed for traditional computer vision research which can aid in this work. 3 Acknowledgment We would like to thank Philip Heermann for supporting of fundamental research between wireless sensor networks and imaging. His ability to see the promise of the integration of computer vision and wireless sensor networks is greatly appreciated. We would like to thank Regan Stinnett for his constant belief and innovative approaches to wireless sensor networks. His insight into the real customer needs was critical in shaping the scope and direction of this work. We would like to thank Ron Kyker for the initial discussions of imaging's role in wireless sensor networks. His knowledge of the hardware constraints were invaluable to formulating a feasible plan of attack.

On computer vision in wireless sensor networks.

Wireless sensor networks allow detailed sensing of otherwise unknown and inaccessible environments. While it would be beneficial to include cameras in a wireless sensor network because images are so rich in information, the power cost of transmitting an image across the wireless network can dramatically shorten the lifespan of the sensor nodes. This paper describe a new paradigm for the incorporation of imaging into wireless networks. Rather than focusing on transmitting images across the network, we show how an image can be processed locally for key features using simple detectors. Contrasted with traditional event detection systems that trigger an image capture, this enables a new class of sensors which uses a low power imaging sensor to detect a variety of visual cues. Sharing these features among relevant nodes cues specific actions to better provide information about the environment. We report on various existing techniques developed for traditional computer vision research which can aid in this work

Performance and security analysis of Gait-based user authentication

Verifying the identity of a user, usually referred to as user authentication, before granting access to the services or objects is a very important step in many applications. People pass through some sorts of authentication process in their daily life. For example, to prove having access to the computer the user is required to know a password. Similarly, to be able to activate a mobile phone the owner has to know its PIN code, etc. Some user authentication techniques are based on human physiological or behavioral characteristics such as fingerprints, face, iris and so on. Authentication methods differ in their advantages and disadvantages, e.g. PIN codes and passwords have to be remembered, eye-glasses must be taken off for face authentication, etc. Security and usability are important aspects of user authentication. The usability aspect relates to the unobtrusiveness, convenience and user-friendliness of the authentication technique. Security is related to the robustness of the authentication method against attacks. Recent advances in electronic chip development offer new opportunities for person authentication based on his gait (walking style) using small, light and cheap sensors. One of the primary advantages of this approach is that it enables unobtrusive user authentication. Although studies on human recognition based on gait indicate encouraging performances, the security per se (i.e. robustness and/or vulnerability) of gait-based recognition systems has received little or no attention. The overall goal of the work presented in this thesis is on performance and security analysis of gait-based user authentication. The nature of the contributions is not on developing novel algorithms, but rather on enhancing existing approaches in gait-based recognition using small and wearable sensors, and developing new knowledge on security and uniqueness of gait. The three main research questions addressed in this thesis are: (1) What are the performances of recognition methods that are based on the motion of particular body parts during gait? (2) How robust is the gait-based user authentication? (3) What aspects do influence the uniqueness of human gait? In respect to the first research question, the thesis identifies several locations on the body of the person, whose motion during gait can provide identity information. These body parts include hip, trouser pockets, arm and ankle. Analysis of acceleration signals indicates that movements of these body segments have some discriminative power. This might make these modalities suitable as an additional factor in multi-factor authentication. For the research question on security as far as we know, this thesis is the first extensive analysis of gait authentication security (in case of hip motion). A gait-based authentication system is studied under three attack scenarios. These attack scenarios include a minimal effort-mimicry (with restricted time and number of attempts), knowing the closest person in the database (in terms of gait similarity) and knowing the gender of the user in the database. The findings of the thesis reveal that the minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users in the database can be a threat to gait-based authentication systems. In the third research question, the thesis provides some insights towards understanding the uniqueness of gait in case of ankle/foot motion. In particular, it reveals the following: heavy footwear tends to diminish foot discriminativeness; a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward direction of the motion; and different parts of the gait cycle provide different level of discrimination. In addition, the thesis proposes taxonomy of user recognition methods based on gait. In addition, the thesis work has also resulted in the follwoing paper which is closely related or overlapping with papers mentioned below. Davrondzhon Gafurov, Kirsi Helkala and Torkjel Søndrol, Biometric Gait Authentication Using Accelerometer Sensor, Journal of Computers, 1(7), pp.51-59, 2006: http://www.academypublisher.com/jcp/vol01/no07/jcp01075159.pdf List of papers. The 8 research papers that constitute the main research part of the thesis are

Gait analysis, modelling, and comparison from unconstrained walks and viewpoints : view-rectification of body-part trajectories from monocular video sequences

L'analyse, la modélisation et la comparaison de la démarche de personnes à l'aide d'algorithmes de vision artificielle a récemment suscité beaucoup d'intérêt dans les domaines d'applications médicales et de surveillance. Il y a en effet plusieurs avantages à utiliser des algorithmes de vision artificielle pour faire l'analyse, la modélisation et la comparaison de la démarche de personnes. Par exemple, la démarche d'une personne peut être analysée et modélisée de loin en observant la personne à l'aide d'une caméra, ce qui ne requiert pas le placement de marqueurs ou de senseurs sur la personne. De plus, la coopération des personnes observées n'est pas requise, ce qui permet d'utiliser la démarche des personnes comme un facteur d'identification biométrique dans les systèmes de surveillance automatique. Les méthodes d'analyse et de modélisation de la démarche existantes comportent toutefois plusieurs limitations. Plusieurs de ces méthodes nécessitent une vue de profil des personnes puisque ce point de vue est optimal pour l'analyse et la modélisation de la démarche. La plupart de ces méthodes supposent également une distance assez grande entre les personnes et la caméra afin de limiter les effets néfastes que la projection de perspective a sur l'analyse et la modélisation de la démarche. Par ailleurs, ces méthodes ne gèrent pas les changements de direction et de vitesse dans les marches. Cela limite grandement les marches pouvant être analysées et modélisées dans les applications médicales et les applications de surveillance. L'approche proposée dans cette thèse permet d'effectuer l'analyse, la modélisation et la comparaison de la démarche de personnes à partir de marches et de points de vue non contraints. L'approche proposée est principalement constituée d'une méthode de rectification du point de vue qui permet de générer une vue fronto-parallèle (vue de profil) de la trajectoire imagée des membres d'une personne. Cette méthode de rectification de la vue est basée sur un modèle de marche novateur qui utilise la géométrie projective pour faire les liens spatio-temporels entre la position des membres dans la scène et leur contrepartie dans les images provenant d'une caméra. La tête et les pieds sont les seuls membres nécessaires à l'approche proposée dans cette thèse. La position et le suivi de ces membres sont automatiquement effectués par un algorithme de suivi des membres développé dans le cadre de cette thèse. L'analyse de la démarche est effectuée par une nouvelle méthode qui extrait des caractéristiques de la démarche à partir de la trajectoire rectifiée des membres. Un nouveau modèle de la démarche basé sur la trajectoire rectifiée des membres est proposé afin de permettre la modélisation et la comparaison de la démarche en utilisant les caractéristiques dynamiques de la démarche. L'approche proposée dans cette thèse est premièrement validée à l'aide de marches synthétiques comprenant plusieurs points de vue différents ainsi que des changements de direction. Les résultats de cette étape de validation montrent que la méthode de rectification de la vue fonctionne correctement, et qu'il est possible d'extraire des caractéristiques de la démarche valides à partir de la trajectoire rectifiée des membres. Par la suite, l'analyse, la modélisation et la comparaison de la démarche de personnes sont effectuées sur des marches réelles qui ont été acquises dans le cadre de cette thèse. Ces marches sont particulièrement difficiles à analyser et à modéliser puisqu'elles ont été effectuées près de la caméra et qu'elles comportent des changements de direction et de vitesse. Les résultats d'analyse de la démarche confirment que les caractéristiques de la démarche obtenues à l'aide de la méthode proposée sont réalistes et sont en accord avec les résultats présentés dans les études cliniques de la démarche. Les résultats de modélisation et de comparaison de la démarche démontrent qu'il est possible d'utiliser la méthode proposée pour reconnaître des personnes par leur démarche dans le contexte des applications de surveillance. Les taux de reconnaissance obtenus sont bons considérant la complexité des marches utilisées dans cette thèse.Gait analysis, modelling and comparison using computer vision algorithms has recently attracted much attention for medical and surveillance applications. Analyzing and modelling a person's gait with computer vision algorithms has indeed some interesting advantages over more traditional biometrics. For instance, gait can be analyzed and modelled at a distance by observing the person with a camera, which means that no markers or sensors have to be worn by the person. Moreover, gait analysis and modelling using computer vision algorithms does not require the cooperation of the observed people, which thus allows for using gait as a biometric in surveillance applications. Current gait analysis and modelling approaches have however severe limitations. For instance, several approaches require a side view of the walks since this viewpoint is optimal for gait analysis and modelling. Most approaches also require the walks to be observed far enough from the camera in order to avoid perspective distortion effects that would badly affect the resulting gait analyses and models. Moreover, current approaches do not allow for changes in walk direction and in walking speed, which greatly constraints the walks that can be analyzed and modelled in medical and surveillance applications. The approach proposed in this thesis aims at performing gait analysis, modelling and comparison from unconstrained walks and viewpoints in medical and surveillance applications. The proposed approach mainly consists in a novel view-rectification method that generates a fronto-parallel viewpoint (side view) of the imaged trajectories of body parts. The view-rectification method is based on a novel walk model that uses projective geometry to provide the spatio-temporal links between the body-part positions in the scene and their corresponding positions in the images. The head and the feet are the only body parts that are relevant for the proposed approach. They are automatically localized and tracked in monocular video sequences using a novel body parts tracking algorithm. Gait analysis is performed by a novel method that extracts standard gait measurements from the view-rectified body-part trajectories. A novel gait model based on body-part trajectories is also proposed in order to perform gait modelling and comparison using the dynamics of the gait. The proposed approach is first validated using synthetic walks comprising different viewpoints and changes in the walk direction. The validation results shows that the proposed view-rectification method works well, that is, valid gait measurements can be extracted from the view-rectified body-part trajectories. Next, gait analysis, modelling, and comparison is performed on real walks acquired as part of this thesis. These walks are challenging since they were performed close to the camera and contain changes in walk direction and in walking speed. The results first show that the obtained gait measurements are realistic and correspond to the gait measurements found in references on clinical gait analysis. The gait comparison results then show that the proposed approach can be used to perform gait modelling and comparison in the context of surveillance applications by recognizing people by their gait. The computed recognition rates are quite good considering the challenging walks used in this thesis