The imaging issue in an automatic face/disguise detection system

Pavlidis, Ioannis; Symosek, P.

doi:10.1109/cvbvs.2000.855246

Cited by 113 publications

(85 citation statements)

References 9 publications

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“…Finally they applied correlation technique to identify the individuals under affine transformation on disguised and varying facial expressions. Further I.Pavlidis and P. Symosek [28] raised the issues and challenges for disguised face detection. They concluded that the upper band of near infrared is particularly advantageous for disguised detection.…”

Section: Face Tampering Detectionmentioning

confidence: 99%

Effect of Face Tampering on Face Recognition

Singh¹,

Singh²

2013

SIPIJ

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Section: Face Tampering Detectionmentioning

confidence: 99%

Effect of Face Tampering on Face Recognition

Singh¹,

Singh²

2013

SIPIJ

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“…Furthermore, thermal infrared is less sensitive to scattering and absorption by smoke or dust and invariant in case of illumination change [32]. It also allows to reveal anatomical information which is very useful in detecting disguised faces [25]. The quality of a multispectral image has great implications on the efficiency of image processing applications.…”

Section: Introductionmentioning

confidence: 99%

Multispectral image denoising with optimized vector non-local mean filter

Said

Hadjidj

Melkemi

et al. 2016

Digital Signal Processing

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Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with additive white Gaussian noise and PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity.

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“…Methods of this category firstly selected specific wavelengths for illumination, and then a classification process was adopted for final decision. Although promising results were given in [7], [12], [20], these techniques all required special expensive hardware, making them not as convenient as other visible light based methods in real world applications.…”

Section: Introductionmentioning

confidence: 99%

Face liveness detection by exploring multiple scenic clues

Yan

Zhang

Lei

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Abstract-Liveness detection is an indispensable guarantee for reliable face recognition, which has recently received enormous attention. In this paper we propose three scenic clues, which are non-rigid motion, face-background consistency and imaging banding effect, to conduct accurate and efficient face liveness detection. Non-rigid motion clue indicates the facial motions that a genuine face can exhibit such as blink, and a low rank matrix decomposition based image alignment approach is designed to extract this non-rigid motion. Face-background consistency clue believes that the motion of face and background has high consistency for fake facial photos while low consistency for genuine faces, and this consistency can serve as an efficient liveness clue which is explored by GMM based motion detection method. Image banding effect reflects the imaging quality defects introduced in the fake face reproduction, which can be detected by wavelet decomposition. By fusing these three clues, we thoroughly explore sufficient clues for liveness detection. The proposed face liveness detection method achieves 100% accuracy on Idiap print-attack database and the best performance on self collected face antispoofing database.

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The imaging issue in an automatic face/disguise detection system

Cited by 113 publications

References 9 publications

Effect of Face Tampering on Face Recognition

Effect of Face Tampering on Face Recognition

Multispectral image denoising with optimized vector non-local mean filter

Face liveness detection by exploring multiple scenic clues

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