Secure mobile services by face and speech based personal authentication

Shen, Linlin; Zheng, Nengheng; Zheng, Songhao; Li, Wei

doi:10.1109/icicisys.2010.5658534

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…In [6], a secure mobile service based on biometric personal identification was proposed. The system makes use of a multimodal approach using face and speech recognition.…”

Section: Multi-biometrics Systemmentioning

confidence: 99%

Palm-print recognition for authentication on smartphones

Javidnia

Ungureanu

Corcoran

2015

2015 IEEE International Symposium on Technology and Society (ISTAS)

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In a world where smart phones are becoming ubiquitous and are more powerful than ever, security is becoming an issue. Equipped with good cameras, smart phones are prime candidates for software designed to recognize objects and people around them. By recognizing biometric patterns such as the face, palm-prints or hand knuckles, smart phones can heighten security standards. Depending on the intended action, specific security requirements should be met. For instance, bank services need a reliable authentication system, whereas unlocking a device can use a lower security feature. For this perspective we are working on a database of palm-prints that have only been y and also designing an authentication system based on these results to be used on smartphones.

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“…In [6], a secure mobile service based on biometric personal identification was proposed. The system makes use of a multimodal approach using face and speech recognition.…”

Section: Multi-biometrics Systemmentioning

confidence: 99%

Palm-print recognition for authentication on smartphones

Javidnia

Ungureanu

Corcoran

2015

2015 IEEE International Symposium on Technology and Society (ISTAS)

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“…Face regions in an image are detected by localizing lip and eye regions using Hough transforms [51], [134]. LBP features are extracted on the detected faces for multimodal authentication in [124], [125]. Deep neural network based AV recognition systems [4] employed LBPs as visual features from face images that are photometrically normalized using the Tan-Triggs algorithm [128].…”

Section: ) Texture Based Featuresmentioning

confidence: 99%

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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“…In [1], a bi-modal, face and speaker, authentication system was evaluated on Phase I of the MOBIO database [2], but this system never ran on a mobile device. By contrast, in [3] a face, speaker and teeth authentication system was developed to run on a HP iPAQ rw6100 (a portable digital assistant), however, the authors evaluated this system on an in-house database consisting of only 50 people.…”

Section: Introductionmentioning

confidence: 99%

Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data

McCool

Marcel

Hadid

et al. 2012

2012 IEEE International Conference on Multimedia and Expo Workshops

194

147

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This paper presents a novel fully automatic bi-modal, face and speaker, recognition system which runs in real-time on a mobile phone. The implemented system runs in real-time on a Nokia N900 and demonstrates the feasibility of performing both automatic face and speaker recognition on a mobile phone. We evaluate this recognition system on a novel publicly-available mobile phone database and provide a well defined evaluation protocol. This database was captured almost exclusively using mobile phones and aims to improve research into deploying biometric techniques to mobile devices. We show, on this mobile phone database, that face and speaker recognition can be performed in a mobile environment and using score fusion can improve the performance by more than 25% in terms of error rates.

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Secure mobile services by face and speech based personal authentication

Cited by 12 publications

References 10 publications

Palm-print recognition for authentication on smartphones

Palm-print recognition for authentication on smartphones

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

Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data

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