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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 26 - 50 of 130

Evaluating automatic face recognition systems with human benchmarks

April 9, 2015
Author(s)
P. Jonathon Phillips, Alice O'Toole
Human face recognition skills are often considered the gold standard against which machines must compete. Over the last two decades, however, international tests of computer-based face recognition algorithms have shown steady improvements in accuracy with

Report on the FG 2015 Video Person Recognition Evaluation

April 2, 2015
Author(s)
P J. Phillips, J. R. Beveridge, Hao Zhang, Bruce A. Draper, Patrick J. Flynn, Zhenhua Feng, Patrik Huber, Josef Kittler, Zhiwu Huang, Shaoxin Li, Yan Li, Meina Kan, Ruiping Wang, Shiguang Shan, Xilin Chen, Haoxiang Li, Hua Gang, Vitomir Struc, Janez Krizaj, Changxing Ding, Dacheng Tao

Generalizing Face Quality and Factor Measures to Video

September 23, 2014
Author(s)
Yooyoung Lee, P. Jonathon Phillips, James Filliben, J. R. Beveridge, Hao H. Zhang
Methods for assessing the impact of factors and image-quality metrics for still face images are well-understood. The extension of these factors and quality measures to faces in video has not, however, been explored. We present a specific methodology for

Identifying Face Quality and Factor Measures for Video

May 20, 2014
Author(s)
Yooyoung Lee, P. Jonathon Phillips, James Filliben, J. R. Beveridge, Hao Zhang
This paper identifies important factors for face recognition algorithm performance in video. The goal of this study is to understand key factors that affect algorithm performance and to characterize the algorithm performance. We evaluate four factor

Adaptive Representations for Video-based Face Recognition Across Pose

March 25, 2014
Author(s)
P. Jonathon Phillips, Yi-Chen Chen, Vishal M. Patel, Rama Chellappa
n this paper, we address the problem of matching faces across changes in pose in unconstrained videos. We pro- pose two methods based on 3D rotation and sparse representation that compensate for changes in pose. The first is Sparse Representation-based

The neural representation of faces and bodies in motion and at rest

January 29, 2014
Author(s)
P J. Phillips, Alice O'Toole, Vaidehi Natu, Xiaobo An, Rice Allyson, James Ryland
The neural organization of person processing relies on brain regions functionally selective for faces or bodies, with a subset of these regions preferring moving stimuli. Although the response properties of the individual areas are well established, less

The role of the face and body in person identification

December 12, 2013
Author(s)
P J. Phillips, Rice Allyson, Alice O'Toole
Information useful for identifying a person can be found both in the face and body. Previous studies indicate that when an entire person is visible, we rely strongly on the face for identification, even if the body can be useful. In this study, we measured

Sparse Embedding-based Domain Adaptation for Object Recognition

December 8, 2013
Author(s)
P J. Phillips, Jingjing Zheng, Rama Chellappa
Domain adaptation algorithms aim at handling the shift between source and target domains. A classifier is trained on images from the source domain; and the classifier recognizes objects in images from the target domain. In this paper, we present a joint

Introduction to Face Recognition and Evaluation of Algorithm Performance

November 14, 2013
Author(s)
P J. Phillips, J. R. Beveridge, Geof H. Givens, Bruce A. Draper, Yui M. Lui, David Bolme
The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. Here we broadly review face recognition from a

Unaware Person Recognition from the Body when Face Identification Fails

November 4, 2013
Author(s)
P J. Phillips, Rice Allyson, Vaidehi Natu, Xiabo An, Alice O'Toole
How do we recognize someone when the face fails? We show that people rely on the body, but are unaware of this. State-of-the-art face recognition algorithms were used to select images of people with no useful identity information in the face. Human

On the Existence of Face Quality Measures

September 30, 2013
Author(s)
P J. Phillips, J. R. Beveridge, David Bolme, Bruce A. Draper, Geof H. Givens, Yui M. Lui, Su L. Cheng, Mohammad N. Teli, Hao Zhang
We address the problem of the existence of quality measures for face recognition. We introduce the concept of an oracle quality measure, which is an optimal quality measure. We approximate oracle quality measures by greedy pruned ordering (GPO). GPO

Propagation of Facial Identities in a Social Network

September 30, 2013
Author(s)
P J. Phillips, Tao Wu, Rama Chellappa
We address the problem of automated face recognition on a social network using a loopy belief propagation frame- work. The proposed approach propagates the identities of faces in photos across social graphs. We characterize performance in terms of

SNoW: Understanding the Causes of Strong, Neutral, and Weak Face Impostor Pairs

September 27, 2013
Author(s)
P J. Phillips, Amanda Sgroi, Patrick J. Flynn, Kevin W. Bowyer
The Strong, Neutral, or Weak Face Impostor Pairs problem was generated to explore the causes and impact of impostor face pairs that span varying strengths of nonmatch. We develop three partitions within the impostor distribution of a given algorithm. The

Biometric Face Recognition: From Classical Statistics to Future Challenges

August 13, 2013
Author(s)
P J. Phillips, Geof H. Givens, J. R. Beveridge, Bruce A. Draper, David Bolme, Yui M. Lui
Automated face recognition has moved from science fiction to reality during the last twenty years. For high quality frontal face images, recognition errors have been cut in half every two years as more sophisticated algorithms are developed. Algorithms and