Multibiometrics Belief Fusion (PDF)

2010

This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on multimodal database containing face and ear images of 400 individuals. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

2006

Audiovisual (AV) biometrics offer complementary information sources, and the use of both voice and facial images for biometric authentication has recently become economically feasible. Therefore, multi-modality adaptive fusion, combining audio and visual information, offers an efficient tool for substantially improving the classification performance. In terms of implementation, we propose to integrate an audio classifier (based on Gaussian mixture models) and a visual classifier (based on FaceIT, a commercially available software) into a well-established mixture-of-expert fusion architecture. In addition, a consistent fusion strategy is introduced as a baseline fusion scheme, which establishes the lower bound of the "consistent region" in the FAR-FRR ROC. Our simulation results indicate that the prediction performance of the proposed adaptive fusion schemes fall in the consistent region. More importantly, the notion of consistent fusion can also facilitate the selection of the best modalities to fuse.

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%

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%

International Journal of Computer Applications, 2016

This paper presents the score level fusion of multimodal biometrics using Hanman-Anirban entropy function. Entropy function captures the uncertainty in the scores. The experimental results ascertain that Entropy based score level fusion outperforms over existing methods of score level fusion such as t-norms, sum and max. We have validated our claim on finger-knuckle-print (FKP) dataset consisting of left index, left middle, right index and right middle FKP. The features of FKPs are extracted using the Gabor Wavelet. The implementation is done using MATLAB and the performance of the proposed technique is evaluated using Receiver Operating characteristics (ROC) curve. The proposed score level fusion approach achieves significant improvement in the performance over the individual FKP. We obtain Genuine acceptance rate of 99% with FAR of 0.001 %.

2009

Multimodal biometric systems integrate information from multiple sources to improve the performance of a typical unimodal biometric system. Among the possible information fusion approaches, those based on fusion of match scores are the most commonly used. Recently, a framework for the optimal combination of match scores that is based on the likelihood ratio (LR) test has been presented. It is based on the modeling of the distributions of genuine and impostor match scores as a finite Gaussian mixture models. In this paper, we propose two strategies for improving the performance of the LR test. The first one employs a voting strategy to circumvent the need of huge datasets for training, while the second one uses a sequential test to improve the classification accuracy on genuine users. Experiments on the NIST multimodal database confirmed that the proposed strategies can outperform the standard LR test, especially when there is the need of realizing a multibiometric system that must accept no impostors.

2015

154203-8585-IJET-IJENS © June 2015 IJENS I J E N S  Abstract— In this paper, the use of finite Gaussian mixture modal (GMM) based Greedy Expectation Maximization (GEM) estimated algorithm for score level data fusion is proposed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuin...

Multimodal biometrics has recently attracted substantial interest for its high performance in biometric recognition system. In this paper we introduce multimodal biometrics for face and palmprint images using fusion techniques at the feature level. Gabor based image processing is utilized to extract discriminant features, while principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce the dimension of each modality. The output features of LDA are serially combined and classified by a Euclidean distance classifier. The experimental results based on ORL face and Poly-U palmprint databases proved that this fusion technique is able to increase biometric recognition rates compared to that produced by single modal biometrics.

TEST Engineering & Management, 2020

The Biometric features are already proven to be robust for forensic and security purposes. The fusion of multiple biometric features in a systematic way looks more promising. This paper addresses combining multiple biometric modals using proposed fusion technique with focus on face and palm print features. Five databases are used for experimentation on face, Face94, Face95 and Face96, FERET and FRGC and two dataset used for the palm print, PolyU and IITD database. Transform based features used are extracted from these databases using Gabor transform, Radon transform, Ridgelet transform and Radon-Gabor transform,, FPLBP, TPLBP. Feature level fusion has been applied using algorithms FFVM, FFVW. As per our study accuracy for fusion using TPLBP is 100 %, for FFVW method. Thus, the above feature level fusion technique is recommended, based on better accuracy and robustness

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. The best modality used as unimodal biometric systems are unable to fully address the problem of higher recognition rate. Multimodal biometric systems are 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. More reliable recognition accuracy and performance are achievable as different modalities were being combined together and different algorithms or techniques were being used. The work presented in this paper focuses on a bimodal biometric system using face and fingerprint. An image enhancement technique (histogram equalization) is used to enhance the face and fingerprint images. Salient features of the face and fingerprint were extracted using the Gabor filter technique. A dimensionality reduction technique was carried out on both images extracted features using a principal component analysis technique. A feature level fusion algorithm (Mahalanobis distance technique) is used to combine each unimodal feature together. The performance of the proposed approach is validated and is effective. https://sites.google.com/site/ijcsis/