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.

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  1. Lü, Shiwen; Da, Feipeng; Deng, Xing: A 3D face recognition method based on regional enhanced local binary pattern (2015)
  2. Karczmarek, Paweł; Pedrycz, Witold; Reformat, Marek; Akhoundi, Elaheh: A study in facial regions saliency: a fuzzy measure approach (2014)
  3. Peng, Xi; Zhang, Lei; Yi, Zhang; Tan, Kok Kiong: Learning locality-constrained collaborative representation for robust face recognition (2014)
  4. Yang, Meng; Zhang, Lei; Feng, Xiangchu; Zhang, David: Sparse representation based Fisher discrimination dictionary learning for image classification (2014)
  5. Jahanbin, Sina; Jahanbin, Rana; Bovik, Alan C.: Passive three dimensional face recognition using iso-geodesic contours and procrustes analysis (2013)
  6. Zou, Hongyan; Da, Feipeng; Wang, Zhaoyang: A 3D face recognition method based on multi-scale Gabor features (2013)
  7. Deng, Weihong; Liu, Yebin; Hu, Jiani; Guo, Jun: The small sample size problem of ICA: a comparative study and analysis (2012)
  8. Lei, Zhen; Zhang, Zhiwei; Li, Stan Z.: Feature space locality constraint for kernel based nonlinear discriminant analysis (2012)
  9. Zou, Hongyan; Da, Feipeng; Li, Xiaoli: 3D face recognition using compositional features from facial curves (2012)
  10. Boom, B.J.; Spreeuwers, L.J.; Veldhuis, R.N.J.: Virtual illumination grid for correction of uncontrolled illumination in facial images (2011)
  11. Chen, C.; Veldhuis, R.: Extracting biometric binary strings with minimal area under the FRR curve for the Hamming distance classifier (2011)
  12. Han, Pang Ying; Jin, Andrew Teoh Beng; Siong, Lim Heng: Eigenvector weighting function in face recognition (2011)
  13. Jing, Xiaoyuan; Li, Sheng; Lan, Chao; Zhang, David; Yang, Jingyu; Liu, Qian: Color image canonical correlation analysis for face feature extraction and recognition (2011)
  14. Lu, Haiping; Plataniotis, Konstantinos N.; Venetsanopoulos, Anastasios N.: A survey of multilinear subspace learning for tensor data (2011)
  15. Peng, Xiaoming; Bennamoun, Mohammed; Mian, Ajmal S.: A training-free nose tip detection method from face range images (2011)
  16. Raghavendra, R.; Dorizzi, Bernadette; Rao, Ashok; Kumar, G.Hemantha: Designing efficient fusion schemes for multimodal biometric systems using face and palmprint (2011)
  17. Spreeuwers, Luuk: Fast and accurate 3D face recognition using registration to an intrinsic coordinate system and fusion of multiple region classifiers (2011)
  18. Deng, Weihong; Hu, Jiani; Guo, Jun; Cai, Weidong; Feng, Dagan: Emulating biological strategies for uncontrolled face recognition (2010)
  19. Deng, Weihong; Hu, Jiani; Guo, Jun; Cai, Weidong; Feng, Dagan: Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach (2010)
  20. Galbally, Javier; McCool, Chris; Fierrez, Julian; Marcel, Sebastien; Ortega-Garcia, Javier: On the vulnerability of face verification systems to hill-climbing attacks (2010)

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