Amioy Kumar

Development of a new cryptographic construct using palmprint-based fuzzy vault

The combination of cryptology and biometrics has emerged as promising component of information security. Despite the current popularity of palmprint biometric, there has not been any attempt to investigate its usage for the fuzzy vault. This paper therefore investigates the possible usage of palmprint in fuzzy vault to develop a user friendly and reliable crypto system. We suggest the use of both symmetric and asymmetric approach for the encryption. The ciphertext of any document is generated by symmetric cryptosystem; the symmetric key is then encrypted by asymmetric approach. Further, Reed and Solomon codes are used on the generated asymmetric key to provide some error tolerance while decryption. The experimental results from the proposed approach on the palmprint images suggest its possible usage in an automated palmprint-based key generation system.

A palmprint-based cryptosystem using double encryption

We propose a novel cryptographic construct incorporating biometrics which insures a secure communication between two channels just by using Palmprint. The cryptosystem utilizes the advantages of both symmetric and asymmetric cryptographic approaches simultaneously; we denote it as double encryption. Any document in communication is first encrypted using symmetric cryptographic approach; the symmetric key involved is then encrypted using Asymmetric approach. Finally, the concept of fuzzy vault is explored to create a secure vault around the asymmetric key. We investigate the possible usage of palmprints in fuzzy vault to develop a user friendly and reliable crypto system. The experimental results from the proposed approach on the real palmprint images suggest its possible usage in an automated palmprint based key generation system.

Biometric Authentication Based on Infrared Thermal Hand Vein Patterns

Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.

Fuzzy binary decision tree for biometric based personal authentication

The use of fuzzy decision trees is yet to be ascertained for the biometric based personal authentications. This paper therefore presents a fuzzy binary decision tree (FBDT) algorithm for decision making on two classes: genuine and imposter using matching scores computed from the biometric databases. The proposed FBDT makes use of two criteria: fuzzy Gini index and fuzzy entropy for the selection of the tree nodes. The fuzzy membership functions can be automatically computed from the training scores and these are employed in two modes: Same function mode, where only one membership function is used for both the classes and Different function mode, where separate functions are used for both the classes. The parameters computed at the learning stages are used for the classification of the claimed identity in any of the two classes. Over-fitting of feature data often results in false branches in the decision trees. So the pruning of the tree is required with the consequent increase in computational complexity. Most of the FBDTs in this work are found to have lesser size than DTs as ascertained from the experimental results. The proposed FBDT is tested on two publically available databases and it fares well over its crisp counterpart.

Ant colony optimization based fuzzy binary decision tree for bimodal hand knuckle verification system

In the recent trends of touch-less biometric authentication systems, hand knuckles from dorsal part of the hand is gaining popularity as a potential candidate for verification/recognition in variety of security applications. However, most of the available knuckle verification systems offer fixed security achieved for desired level of accuracy which cannot meet the varying levels of security requirements. This paper presents a bimodal knuckle verification system which is designed to meet a wide range of applications varying from civilian to high security regions. We use ant colony optimization (ACO) to choose the optimal fusion parameters corresponding to each level of security. The developed verification system utilizes fuzzy binary decision tree (FBDT) which is aimed at decision making in two classes: genuine (accept) and imposter (reject) using matching scores computed from the knuckle database. The FBDT is implemented using fuzzy Gini index for the selection of the tree nodes. The experiments are carried out on four publicly available HongKong PolyU knuckle databases named as: left index, right index, left middle and right middle with four bimodal systems: left-right index, left-right middle, left index-middle and right index-middle. The experimental results from these four bimodal knuckle databases validate the contributions of the proposed work.

Fusion of Hand Based Biometrics Using Particle Swarm Optimization

Multi-modal biometrics has numerous advantages over uni- modal biometric systems. Decision level fusion is the most popular fusion strategy in multimodal biometric systems. Recent research has shown promising performance of hand based biometrics, i.e. palmprint and hand geometry over other biometric modalities. However, the improvement in performance is constrained by the lack of optimal sensor points and fusion strategy. In this paper, we have implemented a particle swarm based optimization technique for selecting optimal parameters through decision level fusion of two modalities: palmprint and hand geometry. The experimental evaluation on a database of 100 users confirms the utility of the decision level fusion using particle swarm optimization.

Adaptive management of multimodal biometrics fusion using ant colony optimization

Adaptive fusion of multimodal biometrics using an ant colony optimization framework.Comparative performance evaluation from decision and score level fusion strategies.Experiments on publicly available databases ascertain superiority of ACO over PSO.Evaluation of ACO framework for fuzzy binary decision tree based verification.Comparative verification using decision tree and decision threshold in ACO framework. This paper presents a new approach for the adaptive management of multimodal biometrics to meet a wide range of application dependent adaptive security requirements. In this work, ant colony optimization (ACO) is employed for the selection of key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment of multimodal biometrics systems. Particle swarm optimization (PSO) has been widely utilized for the optimal selection of these parameters in the earlier attempts in the literature [Veeramachaneni et al., 2005] and [Kumar et al., 2010]. However, in PSO these parameters are computed in continuous domain while they are assumed to be better represented as discrete variables [Kumar et al., 2010]. This paper therefore proposes the use of ACO, in which discrete biometric verification parameters are computed to ensure the optimal performance from the multimodal biometrics system. The proposed ACO based framework is also extended to the pattern classification approach where fuzzy binary decision tree (FBDT) is utilized for two-class biometrics verification. The experimental results are presented on true multimodal systems from various publicly available databases; IITD databases of palmprint and iris, XM2VTS database of speech and faces, and the NIST BSSR1 databases of faces and fingerprint images. Our experimental results presented in this paper suggest that (i) ACO based approach is capable of operating on significantly small error rates in comparison to the widely employed PSO for automated selection of biometrics fusion rules/parameters, (ii) the score-level fusion yields better performance with lower error rate in comparison to the decision level fusion, and finally (iii) the FBDT based classification approach delivers considerably superior performance for the adaptive biometrics verification.

Biometric authentication using finger nail plates

This paper attempts to bring a new inventive and non-mainstream biometric development to the fore. A completely automated and unified approach to authenticate individuals using finger nail plate surface images has been proposed. There has not been any attempt in utilizing the texture and the contour information of the nail-plate for human authentication in literature. This has motivated us to explore the nail plate based identification for security applications and applying approaches that ascertain the best possible performance. The complex technique of Interferometry is perhaps the most widely used approach in the literature to carry out analysis on nail-bed which is the inner part of the nail unit. In this paper, we propose a very convenient and efficient method by acquiring low resolution images of nail plate surface which is the outermost part of the nail unit. The contour and texture characteristics of nail plates from three fingers are represented by the appearance and shape based feature descriptors. The paper presents two ways of integrating the nail-plate features from three fingers: (1) score level rules for fusion of matching scores and (2) the classifier based fusion of matching scores by employing decision tree and support vector machines. The experimental results from 180 users and a total of 2700 nail plate images validate the contributions from this paper.

Decision level biometric fusion using Ant Colony Optimization

This paper presents a decision level fusion scheme for palmprint and hand vein biometrics using an evolutionary technique such as Ant Colony Optimization (ACO) to compute the fusion parameters by selecting them dynamically. A digital camera based imaging set up is employed for acquiring the palmprint images while Infrared thermal imaging is used for hand vein acquisition. A modified block limited phase only correlation (MBLPOC) function is utilized for representing the similarity between the acquired palm images and Gabor wavelets based features are extracted from the vein images. Since both the biometrics are represented in different domain, their integration has been done on the decision level by correctly choosing the decision thresholds; which can optimize the decisions taken by the individual matchers using ACO algorithm. The experimental results carried out on the database of 150 users are promising and thus confirming the usefulness of the proposed fusion system.

Rank Level Integration of Face Based Biometrics

This paper investigates the integration of two modalities: facial thermograms and ear, extracted from the same face, by using rank level fusion scheme. The first modality consists of the infrared thermal faces acquired using infrared camera whereas the second one constitutes point features on the ear imaged using ordinary digital camera. The acquired facial thermo grams and ear images are first normalized by locating ROI and then features are extracted using Haar wavelets and SHIFT (Scale Invariant Feature Transform) respectively. Integration of their associated ranks has been done by using the modified Borda count and logistic regression methods. The proposed authentication system is tested on 500 facial thermo grams and ear images and operates on 98% of genuine acceptance rates (GAR) at 0.1% of false acceptance rate (FAR). Although substantial work remains to be done, yet our results indicate that the rank level integration of facial thermo grams and ear images is poised to provide a promising direction to the face based multimodal biometric systems.