Kevin W. Bowyer - face recognition.

• FRVT 2006 and ICE 2006 Large-Scale Experimental Results
  P. Jonathon Phillips, W. Todd Scruggs, Alice O'Toole, Patrick J. Flynn, Kevin W. Bowyer, Cathy L. Schott and Matthew Sharpe,
  IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.
  pdf of this paper.
  This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. ...
  

• Overview of the Multiple Biometric Grand Challenge,
  P. Jonathon Phillips, Todd Scruggs, Patrick Flynn, Kevin W. Bowyer, Ross Beveridge, Geoff Givens, Bruce Draper and Alice O'Toole,
  International Conference on Biometrics, to appear, June 2009.
  pdf of this paper.
  The goal of the Multiple Biometric Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the constraints in different directions. ...
  

• Introduction to the Special Section of Best Papers from the 2007 Biometrics: Theory, Applications and Systems Conference,
  Kevin W. Bowyer,
  IEEE Transactions on Systems, Man and Cybernetics Pat A, 39 (1), January 2009, 2-3.
  pdf of this paper.
  ... Over 100 papers were submitted to BTAS 07. ... The final result of this process is the set of five papers that appear in this special section. We are particularly fortunate in the way that the five papers in this special section illustrate the breadth of activities in current biometrics research. Face, fingerprint, iris, voice, handwriting, and multimodal biometrics are all represented. ...
  

• Using Multi-Instance Enrollment to Improve Performance 3D Face Recognition,
  Timothy C. Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
  Computer Vision and Image Understanding 112 (2), November 2008, 114-125.
  Preprint pdf version of this paper.
  DOI link to CVIU version of this paper.
  "This paper explores the use of multi-instance enrollment as a means to improve the performance of 3D face recognition. Experiments are performed using the ND-2006 3D face data set which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment outperforms a state-of-the-art component-based recognition approach ..."
  

• Multi-factor Approach to Improving Recognition Performance in Surveillance-quality Video,
  Deborah Thomas, Kevin W. Bowyer and Patrick J. Flynn,
  Biometrics: Theory, Applications and Systems, September 2008, Washington, DC.
  DOI link to IEEE Xplore version of this paper.
  

• Profile Face Detection: A Subset Multi-biometric Approach,
  James Gentile, Kevin W. Bowyer and Patrick J. Flynn,
  Biometrics: Theory, Applications and Systems, September 2008, Washington, DC.
  DOI link to IEEE Xplore version of this paper. >
  

• The Iris Challenge Evaluation 2005,
  P. Jonathon Phillips, Kevin W. Bowyer and Patrick J. Flynn, Xiaomei Liu and W. Todd Scruggs,
  Biometrics: Theory, Applications and Systems, September 2008, Washington, DC.
  DOI link to IEEE Xplore version of this paper.
  

• A Region Ensemble for 3D Face Recognition,
  Timothy Faltemier, Kevin W. Bowyer and Patrick J. Flynn,
  IEEE Transactions on Information Forensics and Security, 3(1):62-73, March 2008.
  DOI link to IEEE Xplore version of this paper.
  ... we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. ... Rank-one recognition rates of 97.2% and verification rates of 93.2% at 0.1% false accept rate are reported and compared to other methods published on the Face Recognition Grand Challenge v2 data set."
  

• Guest Editorial: Introduction to the Special Issue on Recent Advances in Biometric Systems,
  Kevin W. Bowyer, Venu Govindaraju and Nalini Ratha,
  IEEE Transactions on Systems, Man and Cybernetics - B 37 (5), October 2007.
  pdf of this paper.
  "We are pleased to present 14 papers in this special issue devoted to recent advances in biometric systems. ..."
  

• FRVT 2006 and ICE 2006 Large-Scale Results,
  P. J. Phillips, W. T. Scruggs, A. J. O'Toole, P. J. Flynn, K.W. Bowyer, C. L. Schott, and M. Sharpe.
  National Institute of Standards and Technology, NISTIR 7408, http://face.nist.gov, 2007.
  pdf of this report.
  "This report describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. ..."
  Our research group is part of the Multiple Biometric Grand Challenge and Iris Challenge Evaluation support teams.
  

• A Fast Algorithm for ICP-based 3D Shape Biometrics,
  Ping Yan and Kevin W. Bowyer.
  Computer Vision and Image Understanding, 107 (3), 195-202, September 2007.
  pdf of this paper.
  "... we present a novel approach, called "Pre-computed Voxel Nearest Neighbor," to reduce the computational time for shape matching in a biometrics context. The approach shifts the heavy computation burden to the enrollment stage, which is done offline. Experiments in 3D ear biometrics with 369 subjects and 3D face biometrics with 219 subjects demonstrate the effectiveness of our approach."
  

• Biometric Recognition Using Three-dimensional Ear Shape,
  Ping Yan and Kevin W. Bowyer.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (8), 1297-1308, August 2007.
  pdf of this paper.
  "... We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8% for an identification scenario, and equal error rate of 1.2% for a verification scenario on a database of 415 subjects and 1,386 total probes."
  

• Actively Exploring Face Space(s) for Improved Face Recognition,
  Nitesh V. Chawla and Kevin W. Bowyer,
  AAAI 2007, Vancouver, July 2007.
  pdf of this paper.
  "We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We also construct ensembles of classifiers learned from such actively sampled image sets, which further provides improvement in the recognition rates. ..."
  

• Boosting Lite - Handling Larger Datasets and Slower Base Classifiers,
  Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer and W. Philip Kegelmeyer.
  Multiple Classifier Systems (MCS) 2007, Prague, May 2007.
  pdf of this paper.
  "... we examine ensemble algorithms (Boosting Lite and Ivoting) that provide accuracy approximating a single classifier, but which require significantly fewer training examples. ..."
  

• Face Recognition Using 2D, 3D and Infra-Red: Is multi-modal better than multi-sample?
  Kevin W. Bowyer, Kyong I. Chang, Patrick J. Flynn and Xin Chen.
  Proceedings of the IEEE, 94 (11), 2000-2012, November 2006.
  pdf of this paper.
  "We compare the performance improvement obtained by combining three-dimensional or infra-red with normal intensity images (a multi-modal approach) to the performance improvement obtained by using multiple intensity images (a multi-sample approach). Combining results from different types of imagery gives significantly higher recognition rates than are obtained by using a single intensity image. However, significantly higher recognition rates are also obtained by combining results from multiple intensity images."
  

• Multiple Nose Region Matching for 3D Face Recognition under varying facial expression,
  Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn,
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (10), 1695-1700, October 2006.
  pdf of this paper.
  "An algorithm is proposed for 3D face recognition in the presence of varied facial expressions. It is based on combining the match scores from matching multiple overlapping regions around the nose. Experimental results are presented using the largest database employed to date in 3D face recognition studies, over 4,000 scans of 449 subjects. ..."
  

• Multi-modal Biometrics: An Overview,
  Kevin W. Bowyer, et al,
  Second Workshop on Multi-Modal User Authentication (MMUA 2006), May 2006, Toulouse, France.
  pdf of this paper.
  "The topic of multi-modal biometrics has attracted strong interest in recent years. This paper categorizes approaches to multi-modal biometrics based on the biometric source, the type of sensing used, and the depth of collaborative interaction in the processing. This paper also attempts to identify some of the challenges and issues that confront research in multi-modal biometrics."
  This paper represents the invited talk given to open the first day of the workshop.
  

• A Survey of Approaches and Challenges in 3D and Multi-modal 3D+2D Face Recognition,
  Kevin W. Bowyer, Kyong Chang, and Patrick J. Flynn,
  Computer Vision and Image Understanding 101 (1), January 2006, 1-15.
  pdf of this paper.
  "... This survey focuses on face recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images."
  This article was number one on the CVIU "Top 25" list for the quarters October - December 2005 and January - March of 2006, and in the top ten for seven consecutive quarters.
  

• Infra-Red and Visible-Light Face Recognition,
  Xin Chen, Patrick J. Flynn, and Kevin W. Bowyer,
  Computer Vision and Image Understanding 99 (3), September 2005, 332-358.
  pdf of this paper.
  "... We find that in a scenario involving time lapse between gallery and probe, and relatively controlled lighting, (1) PCA-based recognition using visible images outperforms PCA-based recognition using infra-red images, and (2) the combination of PCA-based recognition using visible and infra-red imagery substantially outperforms either one individually..."
  This article was number two on the CVIU "Top 25" list for July - September of 2005.
  

• Overview of the Face Recognition Grand Challenge,
  P. Jonathon Phillips, Patrick J. Flynn, Todd Scruggs, Kevin W. Bowyer, Jin Chang, Kevin Hoffman, Joe Marques, Jaesik Min, and William Worek,
  Computer Vision and Pattern Recognition (CVPR 2005), San Diego, June 2005, I:947-954.
  pdf of this paper.
  Our research group is part of the Face Recognition Grand Challenge support team.
  

• Random Subspaces and Subsampling for 2-D Face Recognition,
  Nitesh V. Chawla and Kevin W. Bowyer,
  Computer Vision and Pattern Recognition (CVPR 2005) , San Diego, June 2005, II: 582-589.
  pdf of this paper.
  

• An Evaluation of Multi-modal 2D+3D Face Biometrics,
  Kyong I. Chang, Kevin W. Bowyer, and Patrick J. Flynn.
  IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (4), April 2005, 619-624.
  pdf of this paper.
  "We report on the largest experimental study to date in multi-modal 2D+3D face recognition ... Major conclusions are: (1) 2D and 3D have similar recognition performance when considered individually, (2) Combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, (3) Combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and (4) Combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multi-modal performance improvement."
  

• Face Recognition Technology and the Security Versus Privacy Tradeoff,
  Kevin W. Bowyer,
  IEEE Technology and Society, Spring 2004, 9-20.
  pdf of this paper.
  "Video surveillance and face recognition systems have become the subject of increased interest and controversy after the September 11 terrorist attacks on the United States. ... This paper analyzes the interplay of technical and social issues involved in the widespread application of video surveillance for person identification."
  This paper received a 2005 Award of Excellence from the Society for Technical Communication.
  

• Comparison and Combination of Ear and Face Images for Appearance-based Biometrics,
  Kyong Chang, Kevin W. Bowyer, Sudeep Sarkar, and Barnabas Victor,
  IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (9), September 2003, 1160-1165.
  pdf of this paper.
  "In the experiments reported here, recognition performance is essentially identical using ear images or face images and combining the two for multimodal recognition results in a statistically significant performance improvement. ... To our knowledge, ours is the only work to present any experimental results of computer algorithms for biometric recognition based on the ear."
  

• Face Recognition Using 2D and 3D Facial Data,
  Kyong I. Chang, Kevin W. Bowyer and Patrick J. Flynn,
  First Workshop on Multi-Modal User Authentication , Santa Barbara, 25-32, December 2003.
  Reprinted in Journal of Intelligence Community Research and Development.
  pdf of this paper.
  "Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is the only such study to incorporate significant time lapse between gallery and probe image acquisition ..."