A Bayesian Method for Infrared Face Recognition

Elguebaly, Tarek; Bouguila, Nizar

doi:10.1007/978-3-642-11568-4_5

Cited by 12 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Progress in comparison with the early work is mainly to be found in the use of more sophisticated statistical techniques. For example, Elguebaly and Bouguila [39] recently described a method based on a generalized Gaussian mixture model, the parameters of which are learnt from a training image set using a Bayesian approach. Although substantially more complex, this approach did not demonstrate a statistically significant improvement in recognition on the IRIS Thermal/Visible database, both methods achieving rank-1 rate of approximately 95%.…”

Section: A Appearance-based Methodsmentioning

confidence: 99%

Infrared face recognition: A literature review

Ghiass

Arandjelovic

Bendada

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Buy / Rent full text

Abstract-Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.

show abstract

Section: A Appearance-based Methodsmentioning

confidence: 99%

Infrared face recognition: A literature review

Ghiass

Arandjelovic

Bendada

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Buy / Rent full text

show abstract

“…Progress in comparison with the early work is mainly to be found in the use of more sophisticated statistical techniques. For example, Elguebaly and Bouguila [56] recently described a method based on a generalized Gaussian mixture model, the parameters of which are learnt from a training image set using a Bayesian approach. Although substantially more complex, this approach did not demonstrate a statistically significant improvement in recognition on the IRIS Thermal/Visible database (see Sec.…”

Section: Recent Advances In Ir Appearance Based Recognitionmentioning

confidence: 99%

Infrared face recognition: A comprehensive review of methodologies and databases

Ghiass

Arandjelovic

Bendada

et al. 2014

Pattern Recognition

106

View full text Add to dashboard Buy / Rent full text

“…Generally, these used holistic face appearance in a simple statistical manner, with little attempt to achieve any generalization, relying instead on the availability of training data with sufficient variability of possible appearance for each subject (Cutler 1996;Socolinsky et al 2001;Selinger and Socolinsky 2004). More sophisticated holistic approaches recently investigated include statistical models based on Gaussian mixtures (Elguebaly and Bouguila 2011) and compressive sensing (Lin et al 2011). Numerous feature based approaches have also been described.…”

Section: Previous Workmentioning

confidence: 99%

Vesselness Features and the Inverse Compositional AAM for Robust Face Recognition Using Thermal IR

Ghiass¹,

Arandjelovic²,

Bendada³

et al. 2013

AAAI

View full text Add to dashboard Buy / Rent full text

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods.

show abstract

A Bayesian Method for Infrared Face Recognition

Cited by 12 publications

References 41 publications

Infrared face recognition: A literature review

Infrared face recognition: A literature review

Infrared face recognition: A comprehensive review of methodologies and databases

Vesselness Features and the Inverse Compositional AAM for Robust Face Recognition Using Thermal IR

Contact Info

Product

Resources

About