Multimodal biometrics by fusion for security using genetic algorithm

Bansal, Nancy; Kaur, Iqbaldeep; Sharma, Dolly

doi:10.1109/ispcc.2017.8269668

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…The MVML oversees botch managing, precision, customer examination and affirmation rate which will be the nice choice as differentiated and the MVSL chiefly based ASR. Nancy Bansal, et al (2018). Mel Frequency Cepstral Coefficients has been used for talk feature extraction and in addition to the present formal method of reasoning is likewise used for teaching reason.…”

Section: Akhmedova Et Al (2018) Differential Evolution Andmentioning

confidence: 99%

Fuzzy Utilization in Speech Recognition and its Different Application

Fernandes¹,

Mannepalli²,

Saravanan³

et al. 2019

IJEAT

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Talk affirmation is one among the basic zones in cutting edge talk process. The examination of talk affirmation may be a bit of an examination for "artificial intelligence" machines that may "hear" and "appreciate" the verbally communicated data. The customary ways for talk affirmation like HMM and DTW, are outrageously inconvenient and time excellent. As such formal Fuzzy justification may be an endeavor in cutting edge talk process for the convincing portrayal of talk affirmation in a couple of utilization. The approach masterminded in the midst of this paper streamlines the utilization of fuzzy in talk affirmation and make the data dealing with time shorter. The case considered in the midst of this paper is that the least mind boggling, i.e., the example of speaker dependence, little vocabulary and disconnected words. There are various spectral and common choices isolated from human talk. The present ways for tendency acknowledgment from voice use basically MFCC and Energy feature. This paper briefs an overview concerning the present work on talk feeling ID strong for completing more examination by feathery approach.

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Section: Akhmedova Et Al (2018) Differential Evolution Andmentioning

confidence: 99%

Fuzzy Utilization in Speech Recognition and its Different Application

Fernandes¹,

Mannepalli²,

Saravanan³

et al. 2019

IJEAT

View full text Add to dashboard Cite

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“…Bansal et al (2017) proposed a multimodal system for face and speech using genetic algorithm. Fuzzy logic is used for training purpose.…”

Section: Literature Surveymentioning

confidence: 99%

“…The experimental results are simulated using MATLAB with recognition rate of 95 per cent and FRR of 4.6 per cent for 125 databases. Bansal et al (2017) proposed a multimodal system for face and speech using genetic algorithm. Fuzzy logic is used for training purpose.…”

Section: Literature Surveymentioning

confidence: 99%

Implementing and analysing FAR and FRR for face and voice recognition (multimodal) using KNN classifier

Kumar

Rao²

2019

IJIUS

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Purpose The purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone. Design/methodology/approach In this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy. Findings The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent. Originality/value The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

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