Face recognition with illumination and pose variations using MINACE filters

David Casasent; Rohit Patnaik

doi:10.1117/12.637219

24 October 2005 Face recognition with illumination and pose variations using MINACE filters

David Casasent, Rohit Patnaik

Proceedings Volume 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision; 600601 (2005) https://doi.org/10.1117/12.637219
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

This paper presents the status of our present CMU face recognition work. We first present a face recognition system that functions in the presence of illumination variations. We then present initial results when pose variations are also considered. A separate minimum noise and correlation energy (MINACE) filter is synthesized for each person. Our concern is face identification and impostor (non-database face) rejection. Most prior face identification did not address impostor rejection. We also present results for face verification with impostor rejection. The MINACE parameter c trades-off distortion-tolerance (recognition) versus discrimination (impostor rejection) performance. We use an automated filter-synthesis algorithm to select c and to synthesize the MINACE filter for each person using a training set of images of that person and a validation set of a few faces of other persons; this synthesis ensures both good recognition and impostor rejection performance. No impostor data is present in the training or validation sets. The peak-tocorrelation energy ratio (PCE) metric is used as the match-score in both the filter-synthesis and test stages and we show that it is better than use of the correlation peak value. We use circular correlations in filter synthesis and in tests, since such filters require one-fourth the storage space and similarly fewer on-line correlation calculations compared to the use of linear correlation filters. All training set images are registered (aligned) using the coordinates of several facial landmarks to remove scale variations and tilt bias. We also discuss the proper handling of pose variations by either pose estimation or by transforming the test input to all reference poses. Our face recognition system is evaluated using images from the CMU Pose, Illumination, and Expression (PIE) database. The same set of MINACE filters and impostor faces are used to evaluate the performance of the face identification and verification systems.

Citation Download Citation

David Casasent and Rohit Patnaik "Face recognition with illumination and pose variations using MINACE filters", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 600601 (24 October 2005); https://doi.org/10.1117/12.637219

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