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Biometrics and Identity Management

Biometrics refers to technologies that measure a person’s physical or behavioral human characteristics to digitally identify a person to grant access to systems, devices or data. Biometric sensor measurements are related to intrinsic human characteristics and fall roughly into two categories: physical identifiers and behavioral identifiers.

Physical identifiers are, for the most part, immutable and device independent such as face, fingerprint, palm, iris, voice, signature, EEG and DNA. Examples of behavioral identifiers are human typing patterns, human gait, mouse movement, websites navigation patterns and the way we use our mobile devices.  

Statler College researchers have specialized in several different biometric research topics, such as identification using different physical and behavioral traits, multimodal identification, multispectral/hyperspectral face recognition, heterogenous identification, biosecurity, video and data analytics, mobile biometrics, soft biometrics and DNA.

Affiliated Faculty

Recent Publications

  1. Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson and Nasser M. Nasrabadi, “Super-resolution Guided Pore Detection for Fingerprint Recognition,” 25th Int. Conf. on Pattern Recognition (ICPR’21), Jan. 10-15, 2021, Milan, Italy.
  2. Veeru Talreja, Matthew Valenti, and Nasser Nasrabadi, “Deep Hashing for Secure Multimodal Biometrics,” IEEE Transactions on Information Forensics and Security,” vol. 16, pp. 1306-1321, 2021.
  3. Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew Valenti, Nasser M. Nasrabadi, “PF-CpGAN: profile to frontal coupled GAN for face recognition in the wild,” IEEE International Joint Conference on Biometrics (IJCB’20), Sept. 28 - October 1, 2020, Houston, Taxes.
  4. Fariborz Taherkhani, Veeru Talreja, Matthew Valenti, and Nasser M. Nasrabadi, “Error Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 3, July 2020.
  5. Seyed Mehdi Iranmanesh, Benjamin Riggan, Shuowen Hu, Nasser M. Nasrabadi, “Coupled Generative Adversarial Network for Heterogeneous Face Recognition,” Image and Vision Computing, vol. 94, February 2020.
  6. Syed Ashiqur Rahman, Donald A. Adjeroh, “Deep Learning Using Convolutional LSTM Estimates Biological Age from Physical Activity,” Scientific reports, 9 (1), 1-15, 2019