A comparative analysis of face recognition performance with visible and thermal infrared imagery

Socolinsky, Diego A.; Selinger, Andrea

doi:10.1109/icpr.2002.1047436

Cited by 118 publications

(54 citation statements)

References 9 publications

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“…Recognition is performed by a nearest neighbor classifier with respect to the combined score. As many previous studies have shown [1,2,4], fusion greatly increases performance.…”

Section: Resultsmentioning

confidence: 99%

“…The second one is a commercial algorithm made available for testing in binary form. 1 The training set for both algorithms was completely disjoint from gallery and probe images, provided by the authors of [4], in time, space and subjects. That is, the training set was collected at an earlier time, in a different location and used a disjoint set of subjects.…”

Section: Algorithms Testedmentioning

confidence: 99%

“…While the volume of literature on the subject is notably smaller than that related to visible face recognition, there is nonetheless a steady stream of research [1,2,3,4,5,6]. These papers have established that thermal imagery of human faces constitutes a valid biometric signature, though mostly relying on databases limited both in size and variability, due to the expense and complexity of extensive data collection.…”

Section: Introductionmentioning

confidence: 99%

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Thermal face recognition over time

Socolinsky

Selinger

2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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We present a comparative study of face recognition performance with visible and thermal infrared imagery, emphasizing the influence of time-lapse between enrollment and testing images. Most previous research in this area, with few exceptions, focused on results obtained when enrollment and testing images were acquired in the same session. We show that the performance difference between visible and thermal recognition in a time-lapse scenario is smaller than previously believed, and in fact is not statistically significant on existing data sets.

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“…Recognition is performed by a nearest neighbor classifier with respect to the combined score. As many previous studies have shown [1,2,4], fusion greatly increases performance.…”

Section: Resultsmentioning

confidence: 99%

Section: Algorithms Testedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Thermal face recognition over time

Socolinsky

Selinger

2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Self Cite

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“…For example, face recognition in nearinfrared (NIR) [17,32] and thermal (THM) [26] spectra has been motivated by the need to determine human identity in nighttime environments [7]. Furthermore, changes in ambient illumination have lesser impact on face images acquired in these spectra than the visible spectrum.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of gender classification methods on thermal and near-infrared face images

Chen

Ross

2011

2011 International Joint Conference on Biometrics (IJCB)

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Automatic gender classification based on face images is receiving increased attention in the biometrics community. Most gender classification systems have been evaluated only on face images captured in the visible spectrum. In this work, the possibility of deducing gender from face images obtained in the near-infrared (NIR) and thermal (THM) spectra is established. It is observed that the use of local binary pattern histogram (LBPH) features along with discriminative classifiers results in reasonable gender classification accuracy in both the NIR and THM spectra.Further, the performance of human subjects in classifying thermal face images is studied. Experiments suggest that machine-learning methods are better suited than humans for gender classification from face images in the thermal spectrum.

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“…Some examples include military applications such as target acquisition [20], autonomous vehicle navigation [66], collision avoidance [88], terrain analysis [69], etc. ; and surveillance applications like pedestrian detection/tracking [4], [52], face detection/recognition [43], [77], etc.…”

Section: Goals and Motivationmentioning

confidence: 99%