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
Automatic multi-view face recognition via 3D model based pose regularization
TL;DR: This paper proposes a fully automatic method for multiview face recognition that outperforms two state-of-the-art face matchers (FaceVACS and MKD-SRC) in automatic multi-view face recognition and can be easily extended to leverage existing face recognition systems for automaticMulti-View face recognition.
Journal ArticleDOI
Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision
Robert Ravnik,Franc Solina +1 more
TL;DR: Age group comparison reveals that children (1–14 years) are the most responsive to the digital signage, and the average attention time is significantly higher when displaying the dynamic content when compared with the static content.
Proceedings ArticleDOI
Facial image super resolution using sparse representation for improving face recognition in surveillance monitoring
TL;DR: A new system which super resolve the image using sparse representation with the specific dictionary involving many natural and facial images followed by Hidden Markov Model and Support vector machine based face recognition.
Proceedings ArticleDOI
Mining weakly labeled web facial images for search-based face annotation
TL;DR: An effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques and an extensive set of empirical studies showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Face recognition by elastic bunch graph matching
TL;DR: A system for recognizing human faces from single images out of a large database containing one image per person, based on a Gabor wavelet transform, which is constructed from a small get of sample image graphs.
Journal ArticleDOI
The FERET database and evaluation procedure for face-recognition algorithms
TL;DR: The FERET evaluation procedure is an independently administered test of face-recognition algorithms to allow a direct comparison between different algorithms and to assess the state of the art in face recognition.
Journal ArticleDOI
Using discriminant eigenfeatures for image retrieval
D.L. Swets,Juyang Weng +1 more
TL;DR: This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection, and demonstrates the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects.
Journal ArticleDOI
Probabilistic visual learning for object representation
Baback Moghaddam,Alex Pentland +1 more
TL;DR: An unsupervised technique for visual learning is presented, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition and is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects.