Poor Quality Fingerprint Recognition Based on Wave Atom Transform

Fingerprint is considered the most practical biometrics due to some specific features which make them widely accepted. Reliable feature extraction from poor quality fingerprint images is still the most challenging problem in fingerprint recognition system. Extracting features from poor fingerprint images is not an easy task. Recently, Multi-resolution transforms techniques have been widely used as a feature extractor in the field of biometric recognition. In this paper we develop a complete and an efficient fingerprint recognition system that can deal with poor quality fingerprint images. Identification of poor quality fingerprint images needs reliable preprocessing stage, in which an image alignment, segmentation, and enhancement processes are performed. We improve a popular enhancement technique by replacing the segmentation algorithm with another new one. We use Waveatom transforms in extracting distinctive features from the enhanced fingerprint images. The selected features are matched throw K-Nearest neighbor classifier techniques. We test our methodology in 114 subjects selected from a very challenges database; CASIA; and we achieve a high recognition rate of about 99.5%

The Two secured Factors of Authentication

One of the popular challenges that faces the Cloud Computing is the authentication problem. Authentication is a mechanism to establish proof of identities to get access of information in the system. There are several solutions to overcome this issue that it can be gathered in three authentication mechanisms which lead to a multifactorial authentication schema. The multi factor is proposed in this paper confirms the user's identity without sending it to the cloud server, which will gain our solution more security and fast response over other solutions that depend on sending the user identity to the server to be authenticated. The User's credentials have never exchanged with the server, it is only saved in the user's mind. We present an efficient authentication schema which based on two authentication factors, the first is the password-based authentication, which is processed in the user's machine, and the second is the biometric-based authentication which adds more secure factors to the authentication process. We use the Elgamal Elliptic Curve Cryptosystem and the symmetric encryption to overcome the security threads on the authentication process. Furthermore; we use the 3D face image as a second challenge response factor in our proposed algorithm. In addition, to save the bandwidth and computation, we use a mobile agent to pass the first factor of the authentication code to be executed in the client machine, and let the second factor to be executed in the cloud server

تقنيات حديثة متعددة الحلول لنظام التعرف على بصمات الاصبع

Using biometrics in recognition of persons has received more and more attention in the last years, due to the necessity to improve the information security and access restrictions of authentication systems. Fingerprint is considered the most practical biometrics due to some specific features which make them widely accepted. Reliable feature extraction from poor quality fingerprint images is still the most challenging problem in fingerprint recognition system. So it needs a lot of pre-processing steps to improve the quality of fingerprint images, then it needs a reliable feature extractors to extract some distinctive features. Recently, multiresolution transforms techniques have been widely used as a feature extractor in the field of biometric recognition. These features can be used as an identification marks in fingerprint recognition. The goal of this thesis is to develop a complete and an efficient fingerprint recognition system that can deal with poor quality fingerprint images. To deal with poor quality fingerprint image with various challenging, a reliable pre-processing stage and an efficient feature extraction are needed. Segmentation is one of the most important pre-processing steps in fingerprint identification followed by image alignment, and enhancement. We improve a common enhancement technique based on STFT analysis by replacing the used segmentation technique which based on thresholding the energy map, with another one based on morphological operation. We use modern multiresolution techniques; Curvelet, Wave Atoms, Shearlet transforms in extracting distinctive features from the enhanced fingerprint images in a new methodology. The selected features are matched through multiple classifier techniques. We use the Minimum Distance Classifier, K-Nearest Neighbour, Self-Organizing Map and Support Vector Machine. We compare between all these classifiers with respect to the various feature extraction techniques. We test our methodology in 114 subjects selected from a very challenges database; CASIA-FingerprintV5; and we achieve a high recognition rate of about 99.5%