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
Face Recognition with Local Line Binary Pattern
Amnart Petpon,Sanun Srisuk +1 more
TL;DR: A novel face representation method for face recognition, called Local Line Binary pattern (LLBP), which is motivated from Local Binary Pattern (LBP), due to it summarizes the local spacial structure of an image by thresholding the local window with binary weight and introducing the decimal number as a texture presentation.
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A comprehensive review of past and present vision-based techniques for gait recognition
TL;DR: In this paper, the authors survey current techniques of gait recognition and modelling with the environment in which the research was conducted and discuss the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement.
Proceedings ArticleDOI
Evaluation of face recognition techniques for application to facebook
Brian C. Becker,Enrique G. Ortiz +1 more
TL;DR: A method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces datasets is presented, and a variety of well-known face recognition algorithms are evaluated against holistic performance metrics of accuracy, speed, memory usage, and storage size.
Proceedings ArticleDOI
Recent advances in biometric person authentication
Jean-Luc Dugelay,J.-C. Junqua,Constantine Kotropoulos,R. Kuhn,Florent Perronnin,Ioannis Pitas +5 more
TL;DR: An overview of voice, fingerprint, and face authentication algorithms is provided for multi-modal authentication in signal processing.
Journal ArticleDOI
DLFace: Deep local descriptor for cross-modality face recognition
TL;DR: A novel cross-modality enumeration loss is proposed to eliminate the modality gap on local patch level, which is then integrated into a convolutional neural networks for deep local descriptor extraction.
References
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