A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

Conti, Vincenzo; Militello, Carmelo; Sorbello, Filippo; Vitabile, Salvatore

doi:10.1109/tsmcc.2010.2045374

Cited by 106 publications

(65 citation statements)

References 21 publications

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“…In this paper [28], novel multimodal biometric identification system suggested based on iris and fingerprint traits. The paper is a modern advancement of multi biometrics, offering an innovative perspective on features fusion.…”

Section: Fusion Prior To Matchingmentioning

confidence: 99%

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Review of Hand Feature of Unimodal and Multimodal Biometric System

Shaikh¹,

Kolekar²

2016

IJCA

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In this age of digital impersonation, biometric techniques are being used increasingly for authentication technique to prevent unauthorized access. As only biometrics, the authentication of individuals using biological identities, can offer true proof of identity. The increasing interest of biometrics is related to security, forensics and remote managing. Extensive research has been conducted in this area with different techniques. In this paper, unimodal, multimodal and fusion techniques are reviewed for authentication. General TermsSecurity

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Section: Fusion Prior To Matchingmentioning

confidence: 99%

“…In [28] finger print matching using segmented region of interest surrounding singularity points, which overcomes the drawback related to the fingerprint minutiae information. In [31] shown that the proposed scheme has less computation time and occupies less memory space.…”

Section: Observationmentioning

confidence: 99%

Review of Hand Feature of Unimodal and Multimodal Biometric System

Shaikh¹,

Kolekar²

2016

IJCA

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“…Jain [20] proposed a latent to full palm print matching system. Conti [21] proposed an innovative multimodal Biometric system based on iris and fingerprint traits. Jain [22] proposed a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases.…”

Section: Apriori Algorithmmentioning

confidence: 99%

A Novel Biometric System based on Hybrid Fusion Speech, Signature and Tongue

Kaur¹,

Singh²

2015

IJCA

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There is urgent need to make behavioural biometric systems more reliable, robust and accurate. This paper presents feature level fusion of offline signature and speech and tongue biometrics. The fusion of speech and signature with tongue made the system more efficient. MFCC is used for feature extraction in speech and in signature DCT is used which is widely used for image and texture feature extraction. While the features of tongue are extracted using SIFT algorithm. Hybrid weighted average using Apirori two item set is applied for the fusion of extracted features of all the modalities. SVC2004 signature database are used for experimental results. Tongue database was collected by capturing pictures using digital camera while for speech CMU_ARCTIC database which is available at Language Technologies Institute at Carnegie Mellon University is used in the work. Features for both noisy and non-noisy samples have been collected separately. Gaussian noise was added to the system to check the performance in noisy environment. The proposed system also works efficiently on filtered noisy modalities with accuracy of 80%. The accuracy of the noiseless system is 88.75% with 0.06 % of FAR and with 0.05% of FRR and the noisy system have FAR 0.05% and FRR 0.15%. The ROC curves are calculated.

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“…Although there has been much research on combining different biometrics for a variety of purposes, there is less consideration on the combining finger print and iris.Baig et al [9] made use of single hamming distance matcher for fusing fingerprint and iris. Conte et al [10] combined feature level information by frequency based approach and Lumini and Nanni [11] presented a case study using SVM. Here we have attempted to combine these two identifiersiris and fingerprint, by transformation based method at confidence level.…”

Section: Review Of Past Workmentioning

confidence: 99%

Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level

Rajbhoj¹,

Mane²

2015

IJCA

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Unimodal Biometric systems depending on information from single trait has many limitations. These are noisy input, inability to enroll into system, unacceptable error rates, spoofing and universality of traits. Multibiometric systems is likely to enhance the recognition accuracy by integration the evidence presented by multiple sources of information. In this paper a multibiometric system using transformation based fusion of two most used biometric traits, fingerprint and iris at confidence level is proposed. Features are extracted from individual biometric modalities by efficient algorithm. These features are first matched with their corresponding templates to compute the corresponding match scores. Match scores obtained from different traits are then transformed using different techniques and combined by simple sum rule to generate a fused match score. The proposed framework is evaluated using standard database. This system overcomes limitation of unimodal biometric system and gives improved performance accuracy. An equal error rate achieved by this system is 0.400. The benefit of this approach is that, it does not require any estimation as in density based approach or a large number of training score as in classifier based approach. Image or feature level fusion is expected to result in better performance, but this approach outperforms feature level as well as decision level fusion of iris and fingerprint.

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A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

Cited by 106 publications

References 21 publications

Review of Hand Feature of Unimodal and Multimodal Biometric System

Review of Hand Feature of Unimodal and Multimodal Biometric System

A Novel Biometric System based on Hybrid Fusion Speech, Signature and Tongue

Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level

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