Multimodal biometric scheme for human authentication technique based on voice and face recognition fusion

Abo-Zaid, A.; Haggag, Ayman; Kasban, H.; Eltokhy, Mostafa A. R.

doi:10.1007/s11042-018-7012-3

Cited by 56 publications

(27 citation statements)

References 28 publications

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“…From the literature, the Gaussian Mixture Model (GMM) [95][96][97] is the method that produced a more robust and better-performing speaker verification system than other speaker modeling approaches. Therefore, it has been extensively used for feature extraction for speaker verification in recent works [1,107,115]. GMM is a probabilistic model describing normally distributed subpopulations within an overall population and was used in voice recognition feature extraction.…”

Section: Speaker Modelmentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

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The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

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Section: Speaker Modelmentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

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“…The experimental results have shown that the approach increases accuracy by 4.14% and 7.86% compared to using face and voice recognition individually, respectively. Face and voice were fused by Abozaid et al [17] to enhance the Wireless Body Area Network (WBAN) security. Mahmoud et al [18] have presented a multimodal biometric authentication method to confirm the identity of a person based on his face and iris features.…”

Section: Related Workmentioning

confidence: 99%

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Cherifi

Amroun

Omar

2021

Multimed Tools Appl

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Nowadays, authentication is required for both physical access to buildings and internal access to computers and systems. Biometrics are one of the emerging technologies used to protect these highly sensitive structures. However, biometric systems based on a single trait enclose several problems such as noise sensitivity and vulnerability to spoof attacks. In this regard, we present in this paper a fully unobtrusive and robust multimodal authentication system that automatically authenticates a user by the way he/she answers his/her phone, after extracting ear and arm gesture biometric modalities from this single action. To deal the challenges facing ear and arm gesture authentication systems in real-world applications, we propose a new method based on image fragmentation that makes the ear recognition more robust in relation to occlusion. The ear feature extraction process has been made locally using Local Phase Quantization (LPQ) in order to get robustness with respect to pose and illumination variation. We also propose a set of four statistical metrics to extract features from arm gesture signals. The two modalities are combined on score-level using a weighted sum. In order to evaluate our contribution, we conducted a set of experiments to demonstrate the contribution of each of the two biometrics and the advantage of their fusion on the overall performance of the system. The multimodal biometric system achieves an equal error rate (EER) of 5.15%.

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“…Most systems utilize both speaker detection and speech detection, but both have various aims and implementation processes and both rely on a human speaking. Speech recognition is widespread and inexpensive, but it is less precise and often takes longer (Abozaid et al, 2019).…”

Section: Voice Recognitionmentioning

confidence: 99%

A Comparative Analysis of Biometrics Types: Literature Review

Rousan¹,

Intrigila²

2020

Journal of Computer Science

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This study provides a thorough analysis of the various biometric types, including their advantages and disadvantages. It compares the different types and provides details about false acceptance and false rejection rates, along with their equations. Biometric screening systems are used to test and identify persons using their physiological or behavioral characteristics. Using only one recognition device is not suitable for identification systems. Multi-factor authentication can improve system security by using two or more kinds of security types, such as passwords and cards, but this is not an ideal security scheme. Passwords may be forgotten or inputted incorrectly, or the identification card may be stolen. To verify and classify people using their physiological attributes, biometric devices are used. These technologies can be classified as either behavioral or physiological biometrics. The former has many shortcomings, such as noisy data, inter-class similarity, intra-class variability, spoofing and universality, which reduce the system's accuracy. The success rate of recognition and verification is, however, substantially improved by multimodal biometric sensing and processing systems, which leverage the detection or processing of two or more behavioral or physiological traits.

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Multimodal biometric scheme for human authentication technique based on voice and face recognition fusion

Cited by 56 publications

References 28 publications

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

A Comparative Analysis of Biometrics Types: Literature Review

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