Audio-visual person verification

Ben-Yacoub, Souheil; Luttin, J.; Jonsson, Kenneth; Matas, Jiřı́; Kittler, Josef

doi:10.1109/cvpr.1999.786997

Cited by 20 publications

(17 citation statements)

References 16 publications

(7 reference statements)

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“…Face recognition is also being used in conjunction with other biometrics such as speech, iris, fingerprint, ear and gait recognition in order to enhance the recognition performance of these methods [8,[22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

557

269

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Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

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Section: Applicationsmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

557

269

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“…Conditions under which such schemes perform well are theoretically understood and have been shown to hold in applications [13]. However, in a very similar fusion scenario [12] (in fact some of the experts combined are also used here) combining high performance speech verification modules and a medium vision module (face recognition), the conditions were violated and none of the aforementioned fusion schemes performed better than the best individual expert.…”

Section: Fusion Experiments On the Xm2vts Database According To The Lmentioning

confidence: 99%

“…This was the theme of the M2VTS project [11], and more recently, also in the context of the XM2VTS database and the Lausanne protocol, succesful integration result were reported in [12].…”

Section: Fusion Experiments On the Xm2vts Database According To The Lmentioning

confidence: 99%

“…Apart from the described DTW-based text dependent verification system based on lip features (SURREYL), 3 face recognition algorithms (SURREY1: based on robust correlation [15], SURREY2: Linear Discriminant Analysis [14] and AUT1 developed at the Aristotle Technical University), and 2 voice-based modalities [12] (IDIAP2 -sphericity and IDIAP3 -HMMs) were considered for the fusion experiments. Their individual performances (Configuration I) on the test set are shown in Table 1.…”

Section: Fusion Experiments On the Xm2vts Database According To The Lmentioning

confidence: 99%

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Fusion of Talking Face Biometric Modalities for Personal Identity Verification

Sanchez

Kittler

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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We describe a personal identity verification system based on lip dynamics biometric. The lip shape is represented in terms of a B-spline model, tracked over time. The coordinates of the 11 control points of the B-spline model are used as features for each frame. An utterance consisting of N frames produces a sequence of 22 dimensional feature vectors that is matched to the template using dynamic time warping. The verification error rate achived by the systems on the XM2VTS database is about 14%. By fusing the system with face and voice biometrics the error rate is reduced to a fractiopn of one percent.

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“…One approach to improving accuracy and robustness of such systems is by combining different modalities, for example voice and face data as in [2]. Although this type of multimodal approach can achieve significant performance improvement, it is still desirable to concentrate on biometric subsystem performance in order to further reduce error rates.…”

Section: Introductionmentioning

confidence: 99%