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
Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones
Kwang-Ting Cheng,Yi-Chu Wang +1 more
TL;DR: This work uses face recognition as an application driver for face recognition and implementations on a smartphone reveals that, utilizing the mobile GPU as a co-processor can achieve significant speedup in performance as well as substantial reduction in total energy consumption, in comparison with a mobile-CPU-only implementation on the same platform.
Proceedings ArticleDOI
Hallucinating faces: TensorPatch super-resolution and coupled residue compensation
Wei Liu,Dahua Lin,Xiaoou Tang +2 more
TL;DR: A new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models, and develops an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space.
Journal ArticleDOI
Robust Kernel Representation With Statistical Local Features for Face Recognition
TL;DR: A kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images.
Journal ArticleDOI
Multiple Representations-Based Face Sketch–Photo Synthesis
TL;DR: A novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch that combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches.
Proceedings ArticleDOI
Robust Statistical Face Frontalization
TL;DR: A novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only and an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised.
References
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Journal ArticleDOI
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