Multimodal Biometric System Using Speech and Signature Modalities

Kaur, Mandeep; Girdhar, Akshay; Kaur, Manvjeet

doi:10.5120/962-1339

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…Scale invariant feature transform(SIFT)algorithm is used for feature extraction of signature, key points are detected at multiple scales. Mel Frequency Cepstral Coefficients(MFCC) algorithm [7] is used for feature extraction of speech, which takes the human perception sensitivity with respect to frequencies and reduces the blurrness of spectral and active Shape Model(ASM) [4] is used for feature extraction of palmprint, which locates the landmark points of any statistically image. Then these extracted features from speech, signature and palmprint are fused at feature level using sum rule.…”

Section: Design and Implementation 21 Proposed Workmentioning

confidence: 99%

Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)

Kaur¹,

Kaur²

2014

IJCA

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Biometric system based on unimodel biometrics are not able to meet the desired performance requirements for security purpose due to problems such as intra-class variations, noisy data, unacceptable error rates and low performance. Multimodal biometrics refers to the use of two or more biometrics modalities in a single identification system. The combination of different modalities is used to improve the recognition accuracy. In this proposed work, for identification based multimodal offline signature verification system, speech verification system and palmprint are combined to enhance the security and accuracy. Signature and speech are naturally produced and palmprint cannot be changed or lost. SIFT is used to extract the features of offline signature, MFCC is used to extract the features of speech and ASM is used to extract the features of palmprint. Using a technique named sum rule uses fusion at feature level in this work. To enhance the accuracy the nature based Cuckoo Search Algorithm is used to extract the best values as optimization technique. The experimental results demonstrated that the proposed multimodal biometrics system achieves a recognition accuracy of 100% with false rejection rate (FRR) of = 0.0% & false acceptance rate (FAR) of = 0.0%.

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Section: Design and Implementation 21 Proposed Workmentioning

confidence: 99%

Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)

Kaur¹,

Kaur²

2014

IJCA

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“…Although this combination of multimodal enhances security and accuracy, yet the complexity of the system increases due to increased number of features extracted out of the multiple samples and suffers from additional cost in terms of acquisition time [9]. So these days the key issue is at what degree features are to be extracted and how the cost factor can be minimized, as the number of features increases the variability of the intra-personal samples due to greater lag times in between consecutive acquisitions of the sample also increases.…”

Section: Choice Of Modalitymentioning

confidence: 99%

“…Among these the major consideration is on feature extraction. As the number of features increases, the intrapersonal model variability issue arises, which is detrimental to system performance and chances of forgery will also increase [9].…”

Section: Design and Implementationmentioning

confidence: 99%

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

Kaur¹,

Kaur²,

Singh³

2013

IJCA

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“…P. S. Sanjekar et al [9] Presents an overview of multimodal biometrics, includes the block diagram of general multimodal biometrics, modules of multimodal biometric system, different levels of fusion in multimodal biometrics with related work also covered. Mandeep Kaur et al [10] discusses about Multimodal Biometric System such as signature and speech modalities which are used to overcome some of the problems of uni-modal systems like noise in sensed data, intra-class variations, distinctiveness, and spoof attacks.…”

Section: Introductionmentioning

confidence: 99%

Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems

Sarwat¹

2017

MCS

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Abstract:The paper proposes a likelihood ratio fusion of face, voice and signature multimodal biometrics verification application systems. Figueiredo-Jain (FJ) estimation algorithm of finite Gaussian mixture modal (GMM) is employed. 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 genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-modal database based on face, signature and voice modalities.

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Multimodal Biometric System Using Speech and Signature Modalities

Cited by 14 publications

References 5 publications

Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)

Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)

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

Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems

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