Face recognition from synchronised visible and near-infrared images

Hizem, Walid; Allano, Lorène; Mellakh, Anouar; Dorizzi, Bernadette

doi:10.1049/iet-spr.2008.0173

Cited by 20 publications

(11 citation statements)

References 12 publications

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“…In [13], a synchronized visible and near-infrared database is presented, where the authors notice the stability of the performance of all the tested algorithms on infrared images upon illumination variation, and the improvement in performance that results from the fusion of these two different kinds of images. In [14], the authors found that while multi-session thermal face recognition under controlled indoor illumination was statistically poorer than visible recognition with two standard algorithms, significance was substantially reduced with an algorithm more specifically tuned to thermal images.…”

Section: Image Signalsmentioning

confidence: 98%

Beyond Cognitive Signals

Espinosa-Duro¹,

Faundez-Zanuy²,

Mekyska

2010

Cogn Comput

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Although audio-visual human systems have several well-known limitations, artificial sensors can measure information beyond our limits. What would happen if we were able to overcome our limitations? Would we be able to obtain a better knowledge of our environment? Or the information beyond our limits is redundant? In this paper, we compare infrared, thermal and visible images from an information theory point of view. We have acquired a small database and compared several measurements over these images. While infrasounds and ultrasounds are not directly applicable, for instance, to speaker recognition due to the impossibility of human beings generating sounds in these frequencies, this is not the case with image signals beyond the visible spectrum for face recognition. We have observed that visible, near-infrared and thermal images contain a small amount of redundancy (less than 1,55 bits).

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Section: Image Signalsmentioning

confidence: 98%

Beyond Cognitive Signals

Espinosa-Duro¹,

Faundez-Zanuy²,

Mekyska

2010

Cogn Comput

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“…Two face images stemmed from a new sensor that was used to simultaneously capture a visible image and a near infrared one. The experiments performed on IRVI database [6] showed that the EER decreased from 1.02 % for the unimodal system based on the near infrared face images to 0.56 % with the fused system.…”

Section: State Of Art Of Biometric Signal Fusionmentioning

confidence: 99%

Watermarking signal fusion in multimodal biometrics

Haddada¹,

Dorizzi

Amara³

2014

International Image Processing, Applications and Systems Conference

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In this paper, we propose a new approach based on watermarking for fusing biometric modalities. The main idea of the proposed approach is to use the watermark both for security purpose and as an additional information related to the person, therefore increasing the personal data used for verification. The host image of the face is watermarked in the multi-resolution space by the palmprint using a watermarking technique based on wavelet packet decomposition. For the verification stage, the characterization of the watermarked face is provided by Gabor filters while classification is performed by SVMs. The experimental results show that this technique ensures a significant performance improvement in both identity verification and biometric security over the use of a single system.

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“…Fortunately, some problems can be avoided using different types of sensors. For example, infrared sensors are less sensitive to ambient light as they capture the temperature of the body [8]. As a result, by using the infrared spectrum information of the infrared images, face recognition can potentially offer simpler but more robust solutions, improving the recognition performance in uncontrolled environments [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…We compare our method with the face recognition with only visible image or near infrared image. In algorithm 1, the distance between sparse coefficients of the face images according to (8) is applied for face recognition. If we only use the visible or near infrared image, the distance of the face images is computed for face recognition.…”

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confidence: 99%