Video Biometrics

A strong requirement to come up with secure and user- friendly ways to authenticate and identify people, to safeguard their rights and interests, has probably been the main guiding force behind biometrics research. Though a vast amount of research has been done to recognize humans based on still images, the problem is still far from solved for unconstrained scenarios. This has led to an increased interest in using video for the task of biometric recognition. Not only does video provide more information, but also is more suitable for recognizing humans in general surveillance scenarios. Other than the multitude of still frames, video makes it possible to characterize biometrics based on inherent dynamics like gait which is not possible with still images. In this paper, we describe several recent algorithms to illustrate the usefulness of videos to identify humans. A brief discussion on remaining challenges is also included.

[1]  Tsuhan Chen,et al.  Video-based face recognition using adaptive hidden Markov models , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Amit K. Roy-Chowdhury,et al.  Integrating Motion, Illumination, and Structure in Video Sequences with Applications in Illumination-Invariant Tracking , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[5]  Nalini K. Ratha,et al.  Biometric Verification: Looking Beyond Raw Similarity Scores , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[6]  R Chellappa,et al.  Face verification through tracking facial features. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Rama Chellappa,et al.  3D Facial Pose Tracking in Uncalibrated Videos , 2005, PReMI.

[9]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[11]  Rama Chellappa,et al.  Identification of humans using gait , 2004, IEEE Transactions on Image Processing.

[12]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[13]  Rama Chellappa,et al.  Gait Analysis for Human Identification , 2003, AVBPA.

[14]  Rama Chellappa,et al.  A system identification approach for video-based face recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[15]  Sudeep Sarkar,et al.  Improved gait recognition by gait dynamics normalization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[17]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Rama Chellappa,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2022 .