Efficient Biometric System Research Articles

Intelligent Integration and Fusion of Multimodal Biometric Systems

The propagation and the frightening expansion of frauds due essentially to the easy access to high technologies make it difficult or sometimes impossible to detect these frauds and impostures. Therefore, it has become important, if not urgent, to develop identification techniques and tools that are more robust to attacks, more precise and more efficient. There are currently in Europe and some American countries very efficient biometric systems that combine two (2) modalities (photos and fingerprints in the case of biometric passports), but they remain very vulnerable, notably because of acquisition problems, data quality or the non-permanence of certain biometrics. Therefore, the systems based on biometric modality has been growing in the world for a decade. The processes of identification and identity verification of individuals have a very wide spectrum of applications in modern society and are becoming very relevant. However, the field of multi-biometrics is not new, many researchers have been working on this topic especially in the last 10 years. Scholar Google lists nearly 6000 publications on the subject, half of which since 2018. Real systems are in production, we can cite the UID program in India to enroll the 1.300 billion inhabitants with the 10 fingerprints and the two Iris. Most of the techniques consisted in generalizing classical biometric systems (with one modality) by attribute fusion (concatenation of data, statistical reduction...), by score fusion (often performed by summing up the comparison scores from each biometric system) or by decision fusion (majority vote most often). The objective of our work is to propose new techniques of fusion & multimodal biometric integration by using artificial intelligence tools and especially the application of fusion techniques such as: Brute Force Search, Support Vector Machines, neural networks, fuzzy systems, neuro-fuzzy systems, Genetic Algorithms, Particle Swarm Optimization.

CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

Biometric Systems Interaction Assessment: The State of the Art

The design and implementation of effective and efficient biometric systems presents a series of challenges to information technology (IT) designers to ensure robust performance. One of the most important factors across biometric systems, aside from algorithmic matching ability, is the human interaction influence on performance. Changes in biometric system paradigms have motivated further testing methods, especially within mobile environments, where the interaction with the device has fewer environmental constraints, which may severely affect system performance. Testing methods involve the need for reflecting on the influence of user-system interaction on the overall system performance in order to provide information for design and testing. This paper reflects on the state of the art of biometric systems interaction assessment, leading to a comprehensive document of the relevant research and standards in this area. Furthermore, the current challenges are discussed and thus we provide a roadmap for the future of biometrics systems interaction research.

Fusion in Multimodal Biometric System: A Review

Objectives: The use of multimodal biometric has been introduced recently owing to use of multiple biometric modalities. Here we perform in-depth review of the various methods used for multimodal biometric technology. Methods/ Statistical Analysis: Here we present a systematic review of various methods used for fusing multiple biometric modalites. Specifically, fusing at various levels such as, before matching and after matching. Score level, feature level, rank level and decision fusion is followed by feature optimization using methods such as genetic algorithms and artificial neural networks. Findings: Single biometric based methods suffer from lack of security and efficiency. This leads to advent of multimodal biometric systems. However, fusing various biometric modalities is being persued with very high interest. We describe the granular nature of several methods used to fuse multiple biometric modalities. A wide range of methods are being employed to fuse biometric data. These methods vary in efficiency and are highly dependant upon the selection of type of biometric chosen for fusion. Application/Improvements: As computational efficiency increases, there increase in more secure and efficient biometric systems that use multiple sources of biometric identification and access authorization.

Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature)

Pattern can be characterized by more or less rich & varied pieces of information of different features. The fusion of these different sources of information can provide an opportunity to develop more efficient biometric system which is known as Multimodal biometric System. Multimodal biometrics is the combination of two or more modalities such as signature and speech modalities. In this work an offline signature verification system and speech verification system are combined as these modalities are widely accepted and natural to produce. This combination of multimodal enhances security and accuracy. In this work, database can be gathered from 14 users. Each user contributes 4 samples of signature & speech also. Forgeries are also added to test system. 14 forgeries are used for testing purpose. SIFT features are extracted for offline signature which results as a feature vector of 128 numbers & MFCC features are extracted for speech which results as a feature vector of 195 numbers. Fusion at feature extraction level is used in this work by using a new technique named msum which can be proposed by combining sum method & mean method. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 98.2% and with false rejection rate (FRR) of = 0.9% & false acceptance rate (FAR) of = 0.9%.