Cryptography and Coding -- Cryptographic Protocols -- Intrusion Detection -- Security Models and Architecture -- Security Management -- Watermarking and Information Hiding -- Web and Network Applications -- Image and Signal Processing -- Applications.

Computational Intelligence and Security

In a biometric sensor system and method, storage of acquired biometric data and/or processing of that data may be shifted from specialized secure processing hardware to host system resources for improved speed and reduced cost of biometric sensor devices and systems. Stored data may be encrypted and/or signed by the specialized secure processing hardware and/or software. A database of authorized biometric data (e.g., patterns or key features representing all or a portion of the fingerprints of authorized users) may be stored on the host system either encrypted or non-encrypted or both. Preliminary matching against a database of many enrolled fingerprints may be accomplished by the system processor to ease the processing burden on the specialized secure processing hardware/software. Final match confirmation remains within exclusive control of the specialized secure processing hardware/software in order to prevent data tampering or other efforts to defeat the security provided by biometric identification.

Secure off-chip processing such as for biometric data

Fingerprint and Footprint verification is an important biometric technique for authentication. In this paper, implementation of feature extraction of fingerprint and footprint in Raspberry pi has been discussed. The raspberry Pi is ARM1176JZFS application processor. It enhances performance, consumes less power, and reduces overall system cost and size. The raspberry Pi is been controlled by a modified version of debian Linux optimized for ARM architecture. The required algorithm for recognizing images for biometric feature extraction is performed using open source OpenCV-2.4.9 in Linux platform using CMake, g++, Makefile. Minutiae extraction for fingerprint and PCA features for footprint using OpenCV is employed. In this paper, review on the feature extraction of fingerprint and footprint recognition has been discussed.

Implementation of fingerprint and newborn footprint feature extraction on Raspberry Pi

In developing countries the newborn swapping and abduction in hospitals are challenging issue and occurs all over the world. Traditional methods have their own drawbacks. Hence, a newborn personal authentication system is proposed for this issue based on bimodal biometric system wherein footprint of newborn and finger print of their mother is used for recognition. This concept is further enhanced by developing a prototype to be implemented on a raspberry Pi (a single board computer). In this paper, SIFT feature extraction, RANSAC algorithm for matching of fingerprint and footprint biometrics using OpenCV on Raspberry pi is implemented. The raspberry Pi is ARM1176JZFS application processor, System on chip (SoC) denoted as Broadcom BCM2835. It enhances performance, consumes less power, and reduces overall system cost and size. The raspberry Pi is been controlled by a modified version of Debian Linux optimized for ARM architecture. The image recognition is performed using open source OpenCV-2.4.9 in Linux platform using CMake, g++, makefile. Thereby the proposed system improves the security system in hospitals / birth centers and provides a low cost solution to the newborn swapping rather than the expensive DNA procedures. In this paper, discusses about the research works carried on hardware as a biometric module to enhance the performance of a standalone device.

A review on implementation of bimodal newborn authentication using raspberry Pi

In current society, the necessity of recognising people is increasing every day. Logical or physical access is restricted to authorised users, which in many cases have to provide tokens where their personal information is stored. At the same time, biometrics proposes a feasible solution for the recognition problem. The combination of both solutions is coming up front. However, up till now, owing to processing restrictions, these tokens are just able to store data and perform the last steps of the biometric recognition process. In this study, the authors propose a new system where tokens are based on hardware/software (HW/SW) co-design, which allows computing most of the biometric process in them. This proposal covers several aspects which these systems are subject to, taking advantages of the two platforms they use for reducing computational time or HW area, and also to increase security or minimise misidentification errors. For testing this proposal, an Iris ID token has been implemented, showing different design alternatives adapted to different work scenarios.

Optimisation of biometric ID tokens by using hardware/software co-design

To heighten the biometrics security level, the biometrics feature extraction and verification need to be performed within smart cards, not in external card readers However, the smart card chip has very limited processing capability, and typical fingerprint feature extraction and verification algorithms may not be executed on a state-of-the-art smart card Therefore, this paper presents a system-on-chip (SoC) implementation of the fingerprint feature extraction algorithm which can be integrated into smart cards To implement the ridge-following algorithm onto the resource-constrained SoCs, the algorithm has been modified to increase the efficiency of the hardware Each functional block of the algorithm has been implemented in hardware or in software by considering its computational complexity, cost and utilization of the hardware, and efficiency of the entire system The proposed system operated in 50MHz, and 20~50 minutiae could be extracted from typical 248timestimes292 fingerprint images in real time with our small area implementation (97K gates) Our current implementation developed as an IP for SoCs targeted for ARM CPU and AMBA bus can also be extended for many other smart card configurations

A VLSI Implementation of Minutiae Extraction for Secure Fingerprint Authentication

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A VLSI Implementation of Minutiae Extraction for Secure Fingerprint Authentication