A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integra t ing hand geometry features. Unlike other bimodal biometric systems, the users do not have to undergo the inconvenience of usingtwo different sensors since the palmprint and hand geometry features can be acquired from the same image, using a digital camera, at the same time. Each of these gray level images are aligned and then used to extract palmprint and hand geometry features. These features are then examined for their individual and combined performances. The image acquisition setup used in this work is inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users. Our experimental results on the image dataset from 100 users confirm the utility of combining hand geometry features with those from palmprints usinga simple image acquis ition setup.

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The development of accurate and reliable security systems is a matter of wide interest, and in this context biometrics is seen as a highly effective automatic mechanism for personal identification. Among biometric technologies, 1 IrisCode developed by Daugman in 1993 is regarded as a highly accurate approach, being able to support real-time personal identification of large databases. Since 1993, on the top of IrisCode, different coding methods have been proposed for iris and fingerprint identification. In this research, I extend and generalize IrisCode for real-time secure palmprint identification. PalmCode, the first coding method for palmprint identification developed by me in 2002, directly applied IrisCode to extract phase information of palmprints as features. However, I observe that the PalmCodes from the different palms are similar, having many 45 o streaks. Such structural similarities in the PalmCodes of different palms would reduce the individuality of PalmCodes and the performance of

The Multimodal Biometric based user authentication systems are highly secured and efficient to use and place total trust on the authentication server where biometric verification data are stored in a central database. Such systems are prone to dictionary attacks initiated at the server side. In this paper, we propose an efficient approach based on multimodal biometrics (Palmprint and Iris) based user authentication and key exchange system. In this system, texture properties are extracted from the palmprint and iris images are stored as encrypted binary template in the server‟s database, to overcome the dictionary attacks mounted by the server. The image processing techniques are used to extract a biometric measurement from the palmprint and iris. During login procedure the mutual authentication is done between the server and user and a symmetric key is generated on both sides, which could be used for further secure communication between them. Thus meet-in-the middle attack that happens between the user and the server can also be overcome. This system can be directly applied to strengthen existing password or biometric based systems without requiring additional computation.

Identifying a reliable feature is extremely important for all pattern recognition systems. The Gabor filter, which simultaneously captures spatial and frequency information, has been a vital component in numerous systems as a feature extractor. This filter produces three basic features — magnitude, phase, and orientation. Most face recognition methods based on Gabor filters use either the magnitude feature alone or a combination of the phase and magnitude features; very few are purely based on the phase feature, and the orientation feature is ignored. The aim of this paper is to evaluate these three basic features for face recognition using the FERET and AR face databases. The results show that the orientation feature is the most robust and distinctive feature, 20 % and over 10% more accurate than the phase and magnitude features, respectively. 1.