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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Boosting sex identification performance
TL;DR: The AdaBoost based classifiers presented here achieve over 93% accuracy; these match or surpass the accuracies of the SVM-based classifiers, and yield performance that is 50 times faster.
Journal ArticleDOI
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
TL;DR: Experimental results indicate that DCP outperforms the state-of-the-art local descriptors for both face identification and face verification tasks and the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
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Improved gait recognition by gait dynamics normalization
Zongyi Liu,Sudeep Sarkar +1 more
TL;DR: It is shown that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information, and improves performance on the UMD gait data set that exercises time variations for 55 subjects.
Journal ArticleDOI
Learning Discriminant Face Descriptor
TL;DR: This paper proposes a method to learn a discriminant face descriptor (DFD) in a data-driven way and applies it to the heterogeneous (cross-modality) face recognition problem and learns DFD in a coupled way to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem.
Journal ArticleDOI
A Comparative Study of Local Matching Approach for Face Recognition
Jie Zou,Qiang Ji,George Nagy +2 more
TL;DR: A complete face recognition system is implemented by integrating the best option of each step and achieves superior performance on every category of the FERET test: near perfect classification accuracy, and significantly better than any other reported performance on pictures taken several days to more than a year apart.
References
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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.
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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.