Audio–visual person authentication using lip-motion from orientation maps

Faraj, Maycel Isaac; Bigün, Josef

doi:10.1016/j.patrec.2007.02.017

Cited by 41 publications

(17 citation statements)

References 35 publications

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“…Thereby, they relied on small photographs (low resolution) and deformations caused by expression changes. Some approaches involved the speech modality ("talking face") [1,4], because the fused audio-visual features are easier to classify into live/non-live. Within this paradigm, that targets on attacks both via photographs and videos, [3] is of interest on its own because the latter showed the possibility of recognizing utterances (digits 0-9) from lip motion only.…”

Section: Related Workmentioning

confidence: 99%

“…We used the same video database for error quantification in section 3.1. Within the suggested categorization in table 1, [2] investigated countermeasures of type III (exploiting 3D properties), whereas the talking face studies [1,4] dealt with extended type II measures (exploiting mouth movements). The approach of [8] is assignable to all three types of countermeasures but it is unable to spatially localize the origin of temporal variations.…”

Section: Related Workmentioning

confidence: 99%

“…Collect evidence for the liveness of each of the faces. 4. After a period expires, verify the liveness of the face.…”

Section: Verifying Livenessmentioning

confidence: 99%

See 2 more Smart Citations

Verifying liveness by multiple experts in face biometrics

Kollreider

Fronthaler

Bigün

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

103

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Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the "liveness" topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of antispoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Verifying liveness by multiple experts in face biometrics

Kollreider

Fronthaler

Bigün

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

103

View full text Add to dashboard Cite

show abstract

“…Faraj and Bigun [41] described a new identity authentication technique by a synergetic use of lip-motion and speech. The lip-motion is defined as the distribution of apparent velocities in the movement of brightness patterns in an image and is estimated by computing the velocity components of the structure tensor by 1D processing, in 2D manifolds.…”

Section: Audio-visual Speech Recognitionmentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

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“…It has been extensively utilized in the state-of-the art of audiovisual speech recognition. [1] There are several uses of lips contour detection like Audiovisual speech authentication [3], intelligent human-computer interaction Human expression recognition [2] etc. Automatic Speech Recognition (ASR) [4] systems use only Acoustic Information, for this system show poor performance in noisy environment.…”

Section: Introductionmentioning

confidence: 99%

Human Lips-Contour Recognition and Tracing

Tareque¹,

Hasan²

2014

IJARAI

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Abstract-Human-lip detection is an important criterion for many automated modern system in present day. Like computerized speech reading, face recognition etc. system can work more precisely if human-lip can detect accurately. There are many processes for detecting human-lip. In this paper an approach is developed so that the region of a human-lip can be detected, we called it lip contour. For this a region-based Active Contour Model (ACM) is introduced with watershed segmentation. In this model we used global energy terms instead of local energy terms because, global energy gives better convergence rate for malicious environment. At the time of ACM initialization by using H∞ based on Lyapunov stability theory, the system gives more accurate and stable result.

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Audio–visual person authentication using lip-motion from orientation maps

Cited by 41 publications

References 35 publications

Verifying liveness by multiple experts in face biometrics

Verifying liveness by multiple experts in face biometrics

Computational Intelligence in Multimedia Processing: Foundation and Trends

Human Lips-Contour Recognition and Tracing

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