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Tytuł artykułu

Acquisition of databases for facial analysis

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article describes guidelines and recommendations for acquisition of databases for facial analysis. New devices and methods for both face recognition and facial expression recognition are constantly developed. In order to evaluate these devices and methods, dedicated datasets are recorded. Acquisition of a database for facial analysis is not an easy task and requires taking into account multiple issues. Based on our experience with recording databases for facial expression recognition, we provide guidelines regarding the acquisition process. Multiple aspects of such process are discussed in this work, namely selection of sensors and data streams, design and structure of the database, technical aspects, acquisition conditions and design of the user interface. Recommendations how to address these aspects are provided and justified. An acquisition software, designed according to these guidelines, is also discussed. The software was used for recording an extended version of our previous facial expression recognition database and proved to both ensure correct data and be convenient for the recorded subjects.
Rocznik
Strony
3--7
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunication, Department of Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland
Bibliografia
  • [1] A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: A survey,” Pattern Recognit. Lett., vol. 28, no. 14, pp. 1885–1906, Oct. 2007.
  • [2] R. Shoja Ghiass, O. Arandjelović, A. Bendada, and X. Maldague, “Infrared face recognition: A comprehensive review of methodologies and databases,” Pattern Recognit., vol. 47, no. 9, pp. 2807–2824, Sep. 2014.
  • [3] D. Smeets, P. Claes, J. Hermans, D. Vandermeulen, and P. Suetens, “A Comparative Study of 3-D Face Recognition Under Expression Variations,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 5, pp. 710–727, Sep. 2012.
  • [4] A. M. Adeshina, S. H. Lau, and C. K. Loo, “Real-time facial expression recognitions: A review,” in 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, 2009, no. July, pp. 375–378.
  • [5] G. Sandbach, S. Zafeiriou, M. Pantic, and L. Yin, “Static and dynamic 3D facial expression recognition: A comprehensive survey,” Image Vis. Comput., vol. 30, no. 10, pp. 683–697, Oct. 2012.
  • [6] B. Y. L. Li, A. S. Mian, W. Liu, and A. Krishna, “Using Kinect for face recognition under varying poses, expressions, illumination and disguise,” 2013 IEEE Work. Appl. Comput. Vis., pp. 186–192, Jan. 2013.
  • [7] F. Malawski, B. Kwolek, and S. Sako, Using Kinect for facial expression recognition under varying poses and illumination, vol. 8610 LNCS. 2014.
  • [8] T. Kanade and J. F. Cohn, “Comprehensive database for facial expression analysis,” Autom. Face Gesture Recognition, 2000. Proceedings. Fourth IEEE Int. Conf., pp. 46–53, 2000.
  • [9] M. Lyons and S. Akamatsu, “Coding Facial Expressions with GaborWavelets,” third IEEE Conf. Autom. Face Gesture Recognit., pp. 200–205, 1998.
  • [10] S. Gupta, K. R. Castleman, M. K. Markey, and A. C. Bovik, “Texas 3D Face Recognition Database,” Proc. IEEE Southwest Symp. Image Anal. Interpret., pp. 97–100, 2010.
  • [11] A. Savran, N. Alyüz, H. Dibeklioglu, O. Çeliktutan, B. Gökberk, B. Sankur, and L. Akarun, “Bosphorus database for 3D face analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5372 LNCS, pp. 47–56, 2008.
  • [12] X. Zhang, L. Yin, J. F. Cohn, S. Canavan, M. Reale, A. Horowitz, P. Liu, and J. M. Girard, “BP4D-Spontaneous: A high-resolution spontaneous 3D dynamic facial expression database,”Image Vis. Comput., vol. 32, no. 10, pp. 692–706, 2014.
  • [13] D. S. Ma, J. Correll, and B. Wittenbrink, “The Chicago face database: A free stimulus set of faces and norming data.,” Behav. Res. Methods, vol. 47, no. 4, pp. 1122–35, 2015.
  • [14] S. Escalera, O. Nikisins, K. Nasrollahi, M. Greitans, C. Corneanu, M. O. Simón, Z. Sun, H. Li, Y. Sun, and T. B. Moeslund, “Improved RGB-D-T based face recognition,” IET Biometrics, 2016.
  • [15] G. Goswami and M. Vatsa, “RGB-D Face Recognition With Texture and Attribute Features,” IEEE Trans. Inf. Forensics Secur., vol. 9, no. 10, pp. 1629–1640, 2014.
  • [16] T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active Appearance Models,” Proc. Eur. Conf. Comput. Vis., vol. 2, pp. 484–498, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f8503230-9235-403c-8011-599a90d884fb
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