Md. Shohel Sayeed

Biometric template protection using watermarking with hidden password encryption

For quite a few years the biometric recognition techniques have been developed. Here, we briefly review some of the known attacks that can be encountered by a biometric system and some corresponding protection techniques. We explicitly focus on threats designed to extract information about the original biometric data of an individual from the stored data as well as the entire authentication system. In order to address security and privacy concerns, we present a biometric authentication scheme that uses two separate biometric features combined by watermark embedding with hidden password encryption to obtain a non-unique identifier of the personage. Furthermore, to present the performance of the authentication system we provide experimental results. The transformed features and templates trek through insecure communication line like the Internet or intranet in the client-server environment. Our projected technique causes security against attacks and eavesdropping because the original biometric will not be exposed anywhere in the authentication system.

Learning above-and-below relationship for vision-based robot navigation system using Distributed Hierarchical Graph Neuron (DHGN) algorithm

Obstacle avoidance is one of the important considerations in developing a vision-based robot navigation system. For flying robots, the ability to learn the above-and-below relationship for obstacle avoidance is necessary. This paper presents a conceptual work in developing a learning mechanism to identify the above-and-below relationship for obstacle avoidance in vision-based robot navigation system using a pattern recognition algorithm known as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a bio-inspired pattern recognition algorithm that implements learning and memorization through a distributed and hierarchical processing. Preliminary results of simple above-and-below navigation with binary images using DHGN indicate that the scheme is able to produce high recall accuracy for obstacle detection. In addition, the proposed scheme implements a one-shot learning approach that is suitable for realtime deployment in robot navigation system.