An efficient reliability estimation technique for audio-visual person identification

Alam, Mohammad Rafiqul; Bennamoun, Mohammed; Togneri, Roberto; Sohel, Ferdous

doi:10.1109/iciea.2013.6566630

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…Feature-level is not preferable since most vendors of biometric systems do not like to reveal the feature values obtained from their systems [8]. Rank-level fusion has not drawn much interest, since it is only applicable for identification of an user [5]. Amongst the five level of fusions, score-level is preferable since the matching scores obtained from multiple matchers are easy to access and easy to combine.…”

Section: Introductionmentioning

confidence: 97%

“…And, such type of weighting techniques have limitations in the situations where there is no prior information of the genuine/impostor score distributions. Authors in [5] have proposed a technique to estimate the reliabilities of audio and video modalities from the ratio of the mean of top k ranked scores to the mean of the score distribution. The reliabilities of different modalities measured in [5] are mapped to modality weights.…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [5] have proposed a technique to estimate the reliabilities of audio and video modalities from the ratio of the mean of top k ranked scores to the mean of the score distribution. The reliabilities of different modalities measured in [5] are mapped to modality weights. The technique in [5] is complex, since it requires a priori knowledge of the rankings of matching scores.…”

Section: Introductionmentioning

confidence: 99%

“…The reliabilities of different modalities measured in [5] are mapped to modality weights. The technique in [5] is complex, since it requires a priori knowledge of the rankings of matching scores. In this paper, a simple weight estimation technique based on the reliabilities, which are directly computed from the raw matching scores for score-level fusion in a multi-biometric system, is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…It can be done at five levels, such as sensor level [3], feature level [4], score level [5], rank level [6] or decision level [7] fusions. Sensorlevel and decision-level have too much redundant and rigid information, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Score reliability based weighting technique for score-level fusion in multi-biometric systems

Kabir

Ahmad

Swamy

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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The performance of multiple matchers heavily influence the recognition accuracy of a multi-biometric system under the simple sum-rule-based score-level fusion. In this paper, a weighting technique, referred to as score reliability based weighting (SRBW) technique, is developed to estimate weights for the matchers in order to improve the recognition rate of multi-biometric systems at the score level. In the proposed technique, the reliabilities are computed directly from the raw matching scores obtained from the individual matchers. The proposed weighting technique does not require a priori knowledge of the rankings of matching scores, or the equal error rates, or the genuine/impostor score distributions of the individual matchers used in the system. The experimental results show that the performance of a multi-biometric system using the proposed weighting technique is superior to that of the uni-biometric systems or to that of the multi-biometric systems using the existing weighting techniques in terms of equal error rate and genuine acceptance rate at 1% false acceptance rate.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Score reliability based weighting technique for score-level fusion in multi-biometric systems

Kabir

Ahmad

Swamy

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Deep Boltzmann Machines for i-Vector Based Audio-Visual Person Identification

Alam

Bennamoun

Togneri

et al. 2016

Image and Video Technology

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Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

Mandalapu

Ramachandra

et al. 2021

IEEE Access

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Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.INDEX TERMS Biometrics, audio-visual person recognition, presentation attack detection.

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An efficient reliability estimation technique for audio-visual person identification

Cited by 6 publications

References 21 publications

Score reliability based weighting technique for score-level fusion in multi-biometric systems

Score reliability based weighting technique for score-level fusion in multi-biometric systems

Deep Boltzmann Machines for i-Vector Based Audio-Visual Person Identification

Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

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