The FERET database and evaluation procedure for face-recognition algorithms. The Face Recognition Technology (FERET) program database is a large database of facial images, divided into development and sequestered portions. The development portion is made available to researchers, and the sequestered portion is reserved for testing facerecognition algorithms. The FERET evaluation procedure is an independently administered test of face-recognition algorithms. The test was designed to: (1) allow a direct comparison between different algorithms, (2) identify the most promising approaches, (3) assess the state of the art in face recognition, (4) identify future directions of research, and (5) advance the state of the art in face recognition.

References in zbMATH (referenced in 234 articles )

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  1. Song, Feng-Xi; Zhang, David; Chen, Cai-Kou; Yang, Jing-Yu: Facial feature extraction method based on coefficients of variances (2007) ioport
  2. Song, Fengxi; Zhang, David; Chen, Qinglong; Wang, Jizhong: Face recognition based on a novel linear discriminant criterion (2007) ioport
  3. Verschae, Rodrigo; Ruiz-del-Solar, Javier; Correa, Mauricio: A unified learning framework for object detection and classification using nested cascades of boosted classifiers (2007) ioport
  4. Yeung, Dit-Yan; Chang, Hong; Dai, Guang: Learning the kernel matrix by maximizing a KFD-based class separability criterion (2007)
  5. Zafeiriou, Stefanos; Tefas, Anastasios; Pitas, Ioannis: The discriminant elastic graph matching algorithm applied to frontal face verification (2007)
  6. Zhang, Xiaoxun; Jia, Yunde: A linear discriminant analysis framework based on random subspace for face recognition (2007)
  7. An, Gaoyun; Ruan, Qiuqi: A novel model for independent radial basis function neural networks with multiresolution analysis (2006)
  8. Boulgouris, Nikolaos V.; Plataniotis, Konstantinos N.; Hatzinakos, Dimitrios: Gait recognition using linear time normalization (2006)
  9. Campadelli, Paola; Lanzarotti, Raffaella; Lipori, Giuseppe: Eye localization for face recognition (2006)
  10. Chow, Tommy W. S.; Rahman, M. K. M.: Face matching in large database by self-organizing maps (2006) ioport
  11. Du, Shan; Ward, Rabab: Face recognition under pose variations (2006)
  12. Geng, Xin; Zhou, Zhi-Hua: Image region selection and ensemble for face recognition (2006) ioport
  13. Huang, Jian; Yuen, Pongchi; Chen, Wen-Sheng; Lai, Jianhuang; You, Xinge: Linear and nonlinear feature-based fusion algorithms for face recognition (2006)
  14. Levine, Martin D.; Rajwade, Ajit: Three-dimensional view-invariant face recognition using a hierarchical pose-normalization strategy (2006) ioport
  15. Mu, Xiaoyan; Artiklar, Mehmet; Watta, Paul; Hassoun, Mohamad H.: An rCE-based associative memory with application to human face recognition (2006) ioport
  16. Nam, Mi Young; Bayarsaikhan, Battulga; Sedai, Suman; Rhee, Phill Kyu: Context-awareness based adaptive classifier combination for object recognition (2006)
  17. Sanderson, Conrad; Bengio, Samy; Gao, Yongsheng: On transforming statistical models for non-frontal face verification (2006) ioport
  18. Shen, Linlin; Bai, Li: Information theory for Gabor feature selection for face recognition (2006)
  19. Shih, Peichung; Liu, Chengjun: Face detection using discriminating feature analysis and support vector machine (2006) ioport
  20. Song, Jiatao; Chi, Zheru; Liu, Jilin: A robust eye detection method using combined binary edge and intensity information (2006)

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