Infrared face recognition: A literature review

Ghiass, Reza Shoja; Arandjelović, Ognjen; Bendada, Hakim; Maldague, Xavier

doi:10.1109/ijcnn.2013.6707096

Cited by 51 publications

(33 citation statements)

References 86 publications

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“…Our future work will explore possible applications of the proposed framework to face recognition when the training and query data are separated by a large time gap, which is an outstanding practical and research challenge [19], to the prediction of disease progression patterns [20], [21], [22], and a number of other domains.…”

Section: Discussionmentioning

confidence: 99%

Information and knowing when to forget it

Sharma

Arandjelović

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Abstract-In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps unintuitively) benefit from the latter too. We demonstrate that if forgetting is implemented in a purposeful and date driven manner, there are a number of benefits which can be gained from discarding information. The framework we introduce is a general one and can be used with any baseline predictor of choice. Hence in this sense it is best described as a meta-algorithm. The method we described was developed through a series of steps which increase the adaptability of the model, while being data driven. We first discussed a weakly adaptive forgetting process which we termed passive forgetting. A fully adaptive framework, which we termed active forgetting was developed by enveloping a passive forgetting process with a monitoring, self-aware module which detects contextual changes and makes a statistically informed choice when the model parameters should be abruptly rather than gradually updated. The effectiveness of the proposed metaframework was demonstrated on a real world data set concerned with a challenge of major practical importance: that of predicting currency exchange rates. Our approach was shown to be highly effective, reducing prediction errors by nearly 40%.

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Section: Discussionmentioning

confidence: 99%

Information and knowing when to forget it

Sharma

Arandjelović

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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“…However, state of the art performance in problem domains where similar geometric flexibility is present, such as face recognition, has been achieved with the use of dense local features in the form of local binary patterns (LPBs) [23]. The LPB representation has proven to be very effective across a range of applications, including texture and face recognition [24,25], and numerous others. The elementary local LBP descriptor considers an image patch of size 3 × 3 pixels.…”

Section: Local Binary Patterns (Lbps)mentioning

confidence: 99%

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Fare

Arandjelović

2017

Lecture Notes in Computer Science

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Abstract. Ancient coins are historical artefacts of great significance which attract the interest of scholars, and a large and growing number of amateur collectors. Computer vision based analysis and retrieval of ancient coins holds much promise in this realm, and has been the subject of an increasing amount of research. The present work is in great part motivated by the lack of systematic evaluation of the existing methods in the context of coin grade which is one of the key challenges both to humans and automatic methods. We describe a series of methods -some being adopted from previous work and others as extensions thereof -and perform the first thorough analysis to date.

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“…Automatic face recognition (AFR) refers to the use of computer systems to analyze facial images, from which to extract effective authentication information, used to identify the identity of the measured face of a technology [7]. As one of the most direct, the most natural and the most easily accepted biometrics, automatic face recognition has been studied for more than sixty years, especially in the last two decades, as a global pattern recognition and The research focus in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Improved LTP

Wang¹,

Zhang²,

Wu³

et al. 2018

Proceedings of the International Symposium on Mechanical Engineering and Material Science (ISMEMS 2017)

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Abstract-Localized binary model (LBP) is an efficient local feature description operator. As a nonparametric description operator, it has received more and more attention and has achieved great success in the field of face recognition. In this paper, we introduce only the first-order non-directional feature of LBP operator, and introduce the high-order differential ULDP operator in four directions, and apply the preprocessing method to face recognition. In addition, the threshold for the LBP operator is completely dependent on the defects of the central pixels. In this paper, the local threshold model (ALTP) of the adaptive threshold is proposed. The threshold of the region is automatically generated by calculating the mean and variance of the local region pixels. The experimental results in several commonly used face databases show that ULDP and ALTP in this paper have good robustness to face recognition in nonconstrained environment, especially face recognition with illumination change.

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Infrared face recognition: A literature review

Cited by 51 publications

References 86 publications

Information and knowing when to forget it

Information and knowing when to forget it

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Face Recognition Based on Improved LTP

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