Thermal face recognition over time

Socolinsky, Diego A.; Selinger, Andrea

doi:10.1109/icpr.2004.1333735

Cited by 68 publications

(25 citation statements)

References 8 publications

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“…On the other hand, outdoor recognition performance is worse for both modalities, with a sharper degradation for visible images regardless of the algorithm. In [15], the same authors conclude that recognition based on thermal images does not imply a less visible performance than when several weeks have elapsed between enrollment and testing.…”

Section: Image Signalsmentioning

confidence: 86%

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: 86%

Beyond Cognitive Signals

Espinosa-Duro¹,

Faundez-Zanuy²,

Mekyska

2010

Cogn Comput

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“…Research has been conducted on whether these problems could be overcome by extending the systems with a thermal sensor. [166] shows a significant improvement by fusing the visible and thermal images in a time-lapse experiment. Work has been done on face recognition using both pixel-level fusion [127, 97,19], feature-level fusion [167], and decision-level fusion [145,129,29].…”

Section: Image Fusionmentioning

confidence: 96%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

591

269

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Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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“…Some studies over a lengthy time interval were reported for face recognition. In [13] images of 240 distinct subjects were acquired under controlled conditions, over a period of ten weeks. They showed that there was not a clearly decreasing performance trend over a period of ten weeks and concluded that degradation line is small enough as to be nearly flat over this time period.…”

Section: Introductionmentioning

confidence: 99%