Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TLDR
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.Abstract:
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.read more
Citations
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Proceedings ArticleDOI
Fully automatic pose-invariant face recognition via 3D pose normalization
TL;DR: This paper proposes a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system and outperforms other comparable methods convincingly.
Journal ArticleDOI
Multi-Task Pose-Invariant Face Recognition
TL;DR: A novel face identification framework capable of handling the full range of pose variations within ±90° of yaw is proposed and consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem.
Proceedings ArticleDOI
SCiFI - A System for Secure Face Identification
TL;DR: This work introduces SCiFI, a system for Secure Computation of Face Identification which performs face identification which compares faces of subjects with a database of registered faces in a secure way which protects both the privacy of the subjects and the confidentiality of the database.
Proceedings ArticleDOI
The Visual Object Tracking VOT2013 Challenge Results
Matej Kristan,Roman Pflugfelder,Ale Leonardis,Jiri Matas,Fatih Porikli,Luka Cehovin,Georg Nebehay,Gustavo Fernandez,Toma Vojir,Adam Gatt,Ahmad Khajenezhad,Ahmed Salahledin,Ali Soltani-Farani,Ali Zarezade,Alfredo Petrosino,Anthony Milton,Behzad Bozorgtabar,Bo Li,Chee Seng Chan,Cherkeng Heng,Dale A. Ward,David Kearney,Dorothy Monekosso,Hakki Can Karaimer,Hamid R. Rabiee,Jianke Zhu,Jin Gao,Jingjing Xiao,Junge Zhang,Junliang Xing,Kaiqi Huang,Karel Lebeda,Lijun Cao,Mario Edoardo Maresca,Mei Kuan Lim,Mohamed El Helw,Michael Felsberg,Paolo Remagnino,Richard Bowden,Roland Goecke,Rustam Stolkin,Samantha Yueying Lim,Sara Maher,Sebastien Poullot,Sebastien Wong,Shin'Ichi Satoh,Weihua Chen,Weiming Hu,Xiaoqin Zhang,Yang Li,ZhiHeng Niu +50 more
TL;DR: The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers.
Proceedings ArticleDOI
PFID: Pittsburgh fast-food image dataset
TL;DR: The first visual dataset of fast foods is introduced with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers to stimulate research on fast food recognition for dietary assessment.
References
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