FRGC

Face Recognition Grand Challenge: The FRGC is structured around challenge problems that are designed to challenge researchers to meet the FRGC performance goal. There are three aspects of the FRGC that will be new to the face recognition community. The first aspect is the size of the FRGC in terms of data. The FRGC data set contains 50,000 recordings. The second aspect is the complexity of the FRGC. Previous face recognition data sets have been restricted to still images. The FRGC will consist of three modes: high resolution still images, 3D images, and multi-images of a person. The third new aspect is the infrastructure. The infrastructure for FRGC will be provided by the Biometric Experimentation Environment (BEE), an XML based framework for describing and documenting computational experiments. The BEE will allow the description and distribution of experiments in a common format, recording of the raw results of an experiment in a common format, analysis and presentation of the raw results in a common format, and documentation of the experiment format in a common format. This is the first time that a computational-experimental environment has supported a challenge problem in face recognition or biometrics.


References in zbMATH (referenced in 46 articles )

Showing results 21 to 40 of 46.
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  1. Günlü, Göksel; Bilge, Hasan S.: Shape and texture based face recognition on overlapping 3D sub-regions (2010)
  2. Gupta, Shalini; Markey, Mia K.; Bovik, Alan C.: Anthropometric 3D face recognition (2010) ioport
  3. Jaiswal, Sushma; Bhadauria, Sarita Singh; Jadon, Rakesh Singh; Divakar, Tarun Kumar: Brief description of image based 3D face recognition methods (2010) ioport
  4. Liu, Zhiming; Liu, Chengjun: Fusion of color, local spatial and global frequency information for face recognition (2010)
  5. Poh, Norman; Bourlai, Thirimachos; Kittler, Josef: A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms (2010)
  6. Sala, Llonch R.; Kokiopoulou, E.; Tošić, I.; Frossard, P.: 3D face recognition with sparse spherical representations (2010)
  7. Su, Yu; Shan, Shiguang; Chen, Xilin; Gao, Wen: Integration of global and local features for face recognition (2010)
  8. Yang, Jian; Liu, Chengjun; Zhang, Lei: Color space normalization: enhancing the discriminating power of color spaces for face recognition (2010)
  9. Zou, Weiwen; Yuen, Pong C.: Discriminability and reliability indexes: two new measures to enhance multi-image face recognition (2010)
  10. Al-Osaimi, F.; Bennamoun, M.; Mian, A.: An expression deformation approach to non-rigid 3D face recognition (2009) ioport
  11. Ansari, A-Nasser; Abdel-Mottaleb, Mohamed; Mahoor, Mohammad H.: A multimodal approach for 3D face modeling and recognition using 3D deformable facial mask (2009) ioport
  12. Chen, C.; Veldhuis, R. N. J.; Kevenaar, T. A. M.; Akkermans, A. H. M.: Biometric quantization through detection rate optimized bit allocation (2009)
  13. Jiang, Xudong; Mandal, Bappaditya; Kot, Alex: Complete discriminant evaluation and feature extraction in kernel space for face recognition (2009) ioport
  14. Mahoor, Mohammad H.; Abdel-Mottaleb, Mohamed: Face recognition based on 3D ridge images obtained from range data (2009)
  15. Ruiz-Del-Solar, Javier; Verschae, Rodrigo; Correa, Mauricio: Recognition of faces in unconstrained environments: a comparative study (2009)
  16. Xiao, Bing; Gao, Xinbo; Tao, Dacheng; Li, Xuelong: A new approach for face recognition by sketches in photos (2009)
  17. Xu, Chenghua; Li, Stan; Tan, Tieniu; Quan, Long: Automatic 3D face recognition from depth and intensity Gabor features (2009)
  18. Zhang, Wei; Lin, Zhouchen; Tang, Xiaoou: Tensor linear Laplacian discrimination (TLLD) for feature extraction (2009)
  19. Al-Osaimi, F. R.; Bennamoun, M.; Mian, A.: Integration of local and global geometrical cues for 3D face recognition (2008)
  20. Mian, Ajmal S.; Bennamoun, Mohammed; Owens, Robyn: Keypoint detection and local feature matching for textured 3D face recognition (2008) ioport