Multimodal Biometrics it is: Need for Future Systems

Mishra, Ashish Kumar

doi:10.5120/720-1012

Cited by 52 publications

(33 citation statements)

References 11 publications

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“…Biometric systems are becoming popular as a measure to identify human being by measuring one's physiological or behavioural characteristics. The multimodal biometric systems provide an advantage over the conventional Unimodal biometric systems in various ways [2]. The main goals of multi-modal biometrics are to reduce at least one of the following; FAR (False Accept Rate), FRR (False Reject Rate), FTE (Failure to Enrollment rate) and Susceptibility to artefacts or mimics.…”

Section: Introductionmentioning

confidence: 99%

“…The main goals of multi-modal biometrics are to reduce at least one of the following; FAR (False Accept Rate), FRR (False Reject Rate), FTE (Failure to Enrollment rate) and Susceptibility to artefacts or mimics. But it also increases sensor cost, enrollment time, transit time and system development [2], [3]. Multimodal biometrics system acquires the input from one or more sensors measuring two or more different modalities of biometric data.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Alsubari¹,

Ramteke²

2017

IJCA

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This paper describes the recognition of multimodal biometric systems based on the face and palmprint features. The common feature extraction technique between the palmprint and face recognition system is the Local Binary Pattern. In the palmprint recognition, the region of palm was extracted form the entire hand. The Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) were used to extract the features of palm. The Zernike moments and LBP were used to extract the features of face. The features of palm and face were integrated as feature vector. To evaluated the accuracy of the system, different classifications were applied such as SVM, KNN, Linear Discriminant. This experiment was performed on the ORL and CASIA database. The proposed system was tested on the said database and found to be satisfactory.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Alsubari¹,

Ramteke²

2017

IJCA

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“…These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities.The most compelling reason to combine different modalities is to improve the recognition rate. This can be done when biometric features of different biometrics are statistically independent.The International Committee for Information Technology Standards (INCITS) Technical Committee M1, Biometrics, and researchers have described methods for performing multi-biometric fusion [3]. In general, the use of the terms multimodal or multi-biometric indicates the presence and use of more than one biometric aspect (modality, sensor, instance and/or algorithm) in some form of combined use for making a specific biometric verification/identification decision [3].…”

Section: Introductionmentioning

confidence: 99%

“…This can be done when biometric features of different biometrics are statistically independent.The International Committee for Information Technology Standards (INCITS) Technical Committee M1, Biometrics, and researchers have described methods for performing multi-biometric fusion [3]. In general, the use of the terms multimodal or multi-biometric indicates the presence and use of more than one biometric aspect (modality, sensor, instance and/or algorithm) in some form of combined use for making a specific biometric verification/identification decision [3]. In this work we are using modular PCA technique for feature extraction of face images using this method we locate vital feature points of face including eyeballs, corners of eyes, nostrils and mouth corners.…”

Section: Introductionmentioning

confidence: 99%

Fused Index Code Based Multibiometric Pattern Retrieval Security System

Patil¹

2016

IJIRCCE

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Biometric authentication has been receiving extensive attention over the past decade with increasing demands in automated personal identification. Biometrics is to identify individuals using physiological or behavioral characteristics, such as fingerprint, face, iris, retina, palm-print, etc. Among all the biometric techniques, fingerprint and face recognition [1] is the most popular methods and is successfully used in many applications.Biometric identification system have huge underlying biometric database. In this large identification system, the goal is to determine the identity of a subject from a large set of users already enrolled in biometric database. Though the state-ofart biometric identification algorithm work well for small database in terms of accuracy and response time but fail to scale well for large databases.Classification and indexing can be used to filter the search space during identification process. Classification is procedure where data points are placed into different groups called classes, based on therequantitive information and already classified data points.In identification, the class of query biometric is first identified and then it is compared to each biometric present in the class. In indexing, feature extraction techniques are used and assigned the index value to them using different indexing techniques. The given query is only compared to templates which have comparable index.In this paper we propose an indexing technique for multimodal database which is capable of reducing the response time of biometric identification systems by reducing the search space during identification.

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“…One of the main problems associated with biometric systems is the undesired variations in the biometric data. These variations can arise due to a variety of factors such as sensors used in capturing the biometric data and various non-ideal operating conditions such as background noise and non-uniform illumination [1][2][3][4][5][6][7][8][9]. Multimodal systems are more robust to environmental and sample quality variations due to the presence of multiple sources of evidences [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

Anzar

Sathidevi

2014

EURASIP J. Adv. Signal Process.

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In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The 'best integration weights' are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

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Multimodal Biometrics it is: Need for Future Systems

Cited by 52 publications

References 11 publications

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Fused Index Code Based Multibiometric Pattern Retrieval Security System

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

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