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Search Publications by: P. Jonathon Phillips (Fed)

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Displaying 76 - 100 of 242

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

The Challenge of Face Recognition From Digital Point-and-Shoot Cameras

June 25, 2013
Author(s)
P J. Phillips, J. R. Beveridge, David Bolme, Bruce A. Draper, Geof H. Givens, Yui M. Lui, Hao Zhang, W T. Scruggs, Kevin W. Bowyer, Patrick J. Flynn, Su L. Cheng
Face recognition is appearing in personal and commercial products at an astonishing rate, yet reliable face recognition remains challenging. Users expect a lot; they want to snap pictures and have their friends, family and acquaintances recognized. This

In-plane Rotation and Scale Invariant Clustering and Dictionary Learning

June 3, 2013
Author(s)
P J. Phillips, Challa Sastry, Yi-Chen Chen, Vishal M. Patel, Rama Chellappa
n this paper, we present an approach that simulta- neously clusters images and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed

Video-based Face Recognition via Joint Sparse Representation

April 26, 2013
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Sumit Shekar, Rama Chellappa
In video-based face recognition, a key challenge is in exploiting the extra information available in a video; e.g., face, body, and motion identity cues. In addition, different video sequences of the same subject may contain variations in resolution

VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies

April 22, 2013
Author(s)
Yooyoung Lee, Ross J. Micheals, James J. Filliben, P J. Phillips
The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body

Dictionary Learning from Ambiguously Labeled Data

April 9, 2013
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa
We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative

Video-based Face Recognition via Joint Sparse Representation

January 2, 2013
Author(s)
P J. Phillips, Vishal M. Patel, Yi-Chen Chen, Rama Chellappa
In video-based face recognition, a key challenge is in exploiting the extra information available in a video. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These

Dictionary-based Face Recognition from Video

December 10, 2012
Author(s)
P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Rama Chellappa
The main challenge in recognizing faces in video is effec- tively exploiting the multiple frames of a face and the accompanying dynamic signature. One prominent method is based on extracting joint appearance and behavioral features. A second method models