Human recognition using fusion of IRIS and Ear data

2020

It has been observed that the accuracy of multimodal biometric system is highly dependent on the adequacy of the applied fusion technique. Fusion at sample, template, matching and ranking levels have all proved reasonable contributions to the performance of the multi-modal systems. In this paper, a model that is based on the combination of Principal Component Analysis (PCA) and Stationary Wavelet Transform (SWT) is proposed for the fusion of biometric images. The model comprises image depuration, histogram balancing, pruning and homogenization as well as PCA-based feature extraction stages. The decomposition and fusion of the images (using the extracted features) were based on SWT. The experimental study of the model with standard face and ear images revealed its suitability for obtaining high quality fusion. The obtained Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Standard Deviation (SD) values established the superiority of the proposed model over some related ones

Biometric System are alternates to the traditional identification system. The Paper provides the multiple features based on the biometric system including Physiological and behaviouralchractersitics.like Fingerprints and iris which is used to identify the Fake and Genuine Users..In this paper we propose a Multimodal Biometric System for feature level fusion that combines the information to investigate the integration of fingerprints and Iris . This Proposed system extracts Gabor texture from the preprocessed fingerprints and Iris sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore proposed the wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.This approach is authenticated for their accuracy of Fingerprints virtual database fused with Iris virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a accuracy of 99.53% and with false rejection ratio (FRR) of = 1%

A novel and efficient method of multisensor biometric image fusion of face and palmprint for personal authentication has been presented in this chapter. High-resolution multisensor face and palmprint images are fused using wavelet decomposition process and matching is performed by monotonic-decreasing graph drawn on invariant SIFT features. For matching, correspondence has been established by searching feature points on a pair of fused images using recursive approach based tree traversal algorithm. To verify the identity of a person, test has been performed with IITK multimodal database consisting of face and palmprint samples. The result shows that the proposed method initiated at the low level / semi-sensor level is robust, computationally efficient and less sensitive to unwanted noise confirming the

Iraqi Journal of Information Technology, 2015

A variety of researches Dealt with the fusion of multi-biometrics for identification in different ways and Showed different results. This paper presents novel study on fusion strategies for personal identification using fingerprint and iris biometrics. The purpose of our paper is to investigate whether the integration of iris and fingerprint biometrics can achieve performance that may not be possible using a single biometric technology. We propose to use two activation function wavelet neural network for feature extraction and identification process after segments the fingerprint image into 16 blocks with (128*128) dimensions and segments the iris image into 32 blocks with (128*128) dimensions. The proposed method in this paper involves three steps. First reduced image size using wavelet packet 1-level decomposition , second feature extraction using two activation function wavelet neural network and identification using trained data and correlation for fingerprint and iris separately and finally fusion fingerprint and iris match scores to get the finally score for each person.

2010

The basic aim of a biometric identification system is to discriminate automatically between subjects in a reliable and dependable way, according to a specific-target application. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In this paper, an innovative multimodal biometric identification system based on iris and fingerprint traits is proposed. The paper is a state-of-the-art advancement of multibiometrics, offering an innovative perspective on features fusion. In greater detail, a frequency-based approach results in a homogeneous biometric vector, integrating iris and fingerprint data. Successively, a hamming-distance-based matching algorithm deals with the unified homogenous biometric vector. The proposed multimodal system achieves interesting results with several commonly used databases. For example, we have obtained an interesting working point with FAR = 0% and FRR = 5.71% using the entire fingerprint verification competition (FVC) 2002 DB2B database and a randomly extracted same-size subset of the BATH database. At the same time, considering the BATH database and the FVC2002 DB2A database, we have obtained a further interesting working point with FAR = 0% and FRR = 7.28% ÷ 9.7%.

in these systems.

Security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process.In this paper we propose a Efficient and Robust Multimodal Biometric System for feature level fusion that combines the information to investigate whether the integration of fingerprints and signatures . Proposed system extracts Gabor texture from the preprocessed fingerprints and signatures sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.proposed approach is authenticated for their accuracy on Fingerprints virtual database fused with signature virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.8% and with false rejection rate (FRR) of = 1%

A Biometrics technology is used to conduct the genuineness of identification and authenticity as per the physiological or behavioural statistics. Biometric is a combination of two words „Bio‟ implies physiological aspects, whereas „Metric‟ refers to measure characteristics i.e. personal identification data. Besides safe and convenient, this technology is very reliable and secure. Biometric is very important in today’s world of fast development in computer technology. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and undesirable error rates. Some of the restrictions can be lifted by designing a multimodal biometric system. Multimodal biometrics provides ultra-secure authentication using multiple biometric traits. We discuss here different types of multimodal biometric systems, different decision fusion techniques used in these systems.

2003

User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity.

Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. Even the best modality and unimodal biometric systems were unable to fully address the problem of accuracy and performance in terms of their false accept rate (FAR) and false reject rate (FRR). Although multimodal biometric systems were able to mitigate some of the limitations encountered in unimodal biometric systems, such as non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof attacks, and performance, the issue of low accuracy and performance still persists. In this paper, we review research papers focused on the accuracy and performance enhancement in information fusion of face and fingerprint biometric recognition systems, determine the main features of the selected methods, and then point out their merits and shortcomings. Finally, we propose a novel approach in mitigating the problem of accuracy and performance of information fusion of multimodal biometric systems. This approach makes use of multilayer perceptron neural networks in training and testing of the network while also proposing the use of the most common used modalities (face and fingerprint) in biometric arena.