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Comparison of facial image datasets

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In computer vision, facial images have been used extensively to develop facial recognition systems, face detection, and many other projects that use facial images. This article compares various 3D facial datasets.


Database Subjects Samples per subject Total images Expressions Poses 3D Color Institution License
Bosphorus[1] 105 31-54 4652 34: action units and 6 expressions, labeled; 24 facial landmarks labeled 13 pitch, yaw, and cross rotations Yes; structured light acquisition Yes Bogazici University, Turkey case-by-case, non-commercial, privacy protections[2]
IMPA-FACE3D[3] 38 14 532 neutral face, 6 expressions (joy, sadness, surprise, anger, disgust, fear), and another 5 facial expressions n/a Yes; structured light acquisition Yes IMPA & IME/USP, Brazil ?
York-3DFace[4] 350 15 5250 neutral face, 5 expressions: anger, happiness, sadness, eyes closed, eye-brows raised Uncontrol led up and down Yes ? University of York, United Kingdom ?
ND2006[5] 888 1-63 13450 5: happiness, sadness, surprise, disgust, other n/a Yes ? University of Notre Dame, United States ?
CASIA[6] 123 37-38 4624 5: Anger, smile, laugh, surprise, closed eyes n/a Yes ? Institute of Automation chinese Academy of Sciences, China ?
BU-3DFE[7] 100 25 2500 neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels) n/a Yes ? Binghamton University, United States ?
FRAV3D[8] 106 16 1696 netral face, smaile and mouth and eyes open Left, right, up, down ? Yes; structured light acquisition Yes Rey Juan Carlos University, Spain ?
BJUT-3D-R1[9] 500 4 2000 neutral face, joy, anger and surprise n/a Yes ? Beijing University of Technology, China ?
FRGC v.2[10] 466 1-22 4007 6: anger, happiness, sadness, surprise, disgust, puffy n/a Yes ? National Institute Of Standards and Technology, United States ?
GavabDB[11] 61 9 549 neutral face, smile, frontal accentuated laugh, frontal random gesture Left, right, up, down Yes ? Rey Juan Carlos University, Spain publications must reference; available for immediate download
3D_RMA[12] 100 20+ 9971 Mostly neutral, some unconstrained; neutral are labeled, unconstrained not further labeled head turning from left to right, head nodding, raised/lowered head turning left to right Yes Yes Royal Military Academy, Belgium ?
PUT 120 6 720 n/a? Slight left/right and up/down No ? ? case-by-case, non-commercial, publications must reference; available for download after email correspondence[13]

The above table was collected from the following works:

  • J. P. Mena-Chalco, R. M. Cesar-Jr., and L. Velho. Banco de Dados de Faces 3D. Technical Report 1-8, Institute of Mathematics and Statistics - University of São Paulo, São Paulo, SP, Brazil, January 2011. TR 02.
  • A. Savran, N. Alyüz, H. Dibeklioğlu, O. Celiktutan, B. Gökberk, B. Sankur, and L. Akarun. Bosphorus database for 3D face analysis. Biometrics and Identity Management, v. 5372, pp 47–56, 2008.

It is important to note that, there is a related list of 2D and 3D face databases continuously updated and available at www.face-rec.org/. This page, maintained by M. Grgic and K. Delac, serves as a starting point for researchers in the exploration of knowledge about human faces through computational methods.

References

  1. ^ Savran et al.(2008) A. Savran, N. Alyüz, H. Dibeklioğlu, O. Celiktutan, B. Gökberk, B. Sankur, and L. Akarun. Bosphorus database for 3D face analysis. Biometrics and Identity Management, pp 47–56.
  2. ^ Bosphorus - How to obtain
  3. ^ Mena-Chalco et al.(2008) J.P. Mena-Chalco, R.M. Cesar-Jr, and L. Velho. Banco de dados de faces 3D: IMPA-FACE3D. Technical report, National Institute for Pure and Applied Mathematics - IMPA - VISGRAF Laboratory, Rio de Janeiro, RJ, Brazil. TR01.
  4. ^ Heseltine et al.(2008) T. Heseltine, N. Pears, and J. Austin. Three-dimensional face recognition using combinations of surface feature map subspace components. Image and Vision Computing. v. 26, n. 3, pp 382–396.
  5. ^ Faltemier et al.(2007) T. Faltemier, K. Bowyer, and P. Flynn. Using a multi-instance enrollment representation to improve 3D face recognition. pp 1–6.
  6. ^ Zhong et al.(2007) C. Zhong, Z. Sun, and T. Tan. Robust 3D face recognition using learned visual codebook. In IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR, pp 1–6.
  7. ^ Yin et al.(2006) L.J. Yin, X.Z. Wei, Y. Sun, J. Wang, and M.J. Rosato. A 3D facial expression database for facial behavior research. In 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 211–216.
  8. ^ Conde(2006) C. Conde. Biometría: Reconocimiento facial mediante fusión 2D y 3D. Dykinson SL, Madrid.
  9. ^ Beijing University of Technology(2005) Beijing University of Technology. The BJUT-3D large-scale chinese face database. Technical report, Beijing University of Technology. Technical Report of The Multimedia and Intelligent Software Technology Beijing Municipal Key Laborator.
  10. ^ Phillips et al.(2005) P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. Overview of the face recognition grand challenge. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005, pp 947–954.
  11. ^ Moreno and Sánchez(2004) A.B. Moreno and A. Sánchez. GavabDB: a 3D face database. In Workshop on Biometrics on the Internet, pp. 77–85.
  12. ^ Beumier and Acheroy(2001) C. Beumier and M. Acheroy. Face verification from 3D and grey level clues. Pattern Recognition Letters, 22(12):1321–1329.
  13. ^ PUT license page
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