Personal identification using feature and score level fusion of palm- and fingerprints

Abstract

The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. In this paper, we present multimodal approach for palm-and fingerprints by feature level and score level fusions (sum and product rules). The proposed multimodal systems are tested on a developed database consisting of 440 palm-and fingerprints each of 55 individuals. In feature level fusion, directional energy-based feature vectors of palm-and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palmand fingerprints identifiers with EER of 2.822 and 2.553%, respectively.

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

“…No matter what image enhancement technique is applied it would not be able to compensate for motion blur and de-focused image without affecting the truthfulness of pixels. As mentioned in our previous research on a peg-free system [27], [28], [29] we have advocated that even slight image rotation changes the original image parameters and induces a certain blur which in turn degrades the performance of the system. In order to get a pure image we have endeavored to improve upon the process, the subjects and the capturing device before eventual image acquisition.…”

Age‐invariant face recognition system using combined shape and texture features

“…The PCA is one of the most popular linear techniques for dimensionality reduction. It performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional space is maximised [50]. The reduced feature sets are then normalised separately before concatenating them into a single combined feature vector.…”

Multimodal biometric authentication based on score level fusion of finger biometrics

“…Ghulam et al [18] investigated an approach for personal authentication using both fingerprint and plam features. The consolidated matching scores using the sum and product rule, respectively, improved the recognition performance.…”