Physiology-based face recognition in the thermal infrared spectrum

Pradeep Buddharaju, Ioannis T. Pavlidis, Panagiotis Tsiamyrtzis, Mike Bazakos

Research output: Contribution to journalArticlepeer-review

186 Scopus citations


The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

Original languageEnglish (US)
Pages (from-to)613-626
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
StatePublished - Apr 2007

Bibliographical note

Funding Information:
The authors would like to thank the University of Notre Dame for kindly providing us IR images acquired during different sessions. They would also like to thank Chinmay Manohar from Mayo Clinic and Dr. Jay Murthy from the University of Texas Medical School for their valuable assistance. This research was supported mainly by US National Science Foundation Grant DUE-0313880 in “Collaborative Research: Capacity Expansion in Information Assurance” and NSF Grant IIS-0414754 in “Interacting with Human Physiology.” It


  • Biometrics
  • Face recognition
  • Physiology
  • Thermal infrared
  • Vascular network


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