Abstract
This paper describes the acquisition process and content of a multispectral face database, which can be used to research on face recognition methods dealing with two of the most challenging problems in this area, i.e. partial occlusion and pose variations. Four cameras were synchronized and arranged to simultaneously capture images from visible, thermal, ultraviolet and near-infrared spectra, which had reported promising results for recognizing faces, individually. In order to simulate pose variations, each subject was asked to look forward, up, down, and to the sides, varying the point of view angle. On the other hand, partial occlusion was generated using sunglasses and a paper sheet. Additionally, three lighting changes were also included (halogen, natural and infrared). A total of 306 images were acquired by subject and 31 subjects were recruited. So, the whole database is composed of 9486 images, which are now available to other researchers. Preliminary results showed that spectra variations affect the performance of a deep learning recognition approach. As far as we know, this is the first database of faces including images from those spectra and the other variations simultaneously.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Su, H.R., Chen, K.Y., Wong, W.J., Lai, S.H.: A deep learning approach towards pore extraction for high-resolution fingerprint recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2057–2061. IEEE (2017)
Best-Rowden, L., Jain, A.K.: Longitudinal study of automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2017)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Ghiass, R.S., Arandjelović, O., Bendada, A., Maldague, X.: Infrared face recognition: A comprehensive review of methodologies and databases. Pattern Recogn. 47(9), 2807–2824 (2014)
Arya, S., Pratap, N., Bhatia, K.: Future of face recognition: A review. Procedia Comput. Sci. 58, 578–585 (2015)
Sarfraz, M.S., Stiefelhagen, R.: Deep perceptual mapping for cross-modal face recognition. Int. J. Comput. Vis. 122(3), 426–438 (2017)
Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., Xu, Y.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 156–171 (2017)
Li, S., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 348–353 (2013)
Dhamecha, T.I., Nigam, A., Singh, R., Vatsa, M.: Disguise detection and face recognition in visible and thermal spectrums. In: 2013 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2013)
Espinosa-Duró, V., Faundez-Zanuy, M., Mekyska, J.: A new face database simultaneously acquired in visible, near-infrared and thermal spectrums. Cogn. Comput. 5(1), 119–135 (2013)
Hermosilla, G., Gallardo, F., Farias, G., Martin, C.S.: Fusion of visible and thermal descriptors using genetic algorithms for face recognition systems. Sensors 15(8), 17944–17962 (2015)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)
Martinez, A., Benavente, R.: The AR face database, 1998. Computer Vision Center, Technical report, vol. 3, p. 5 (2007)
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 149–161 (2008)
Chang, H., Harishwaran, H., Yi, M., Koschan, A., Abidi, B., Abidi, M.: An indoor and outdoor, multimodal, multispectral and multi-illuminant database for face recognition. In: 2006 IEEE Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 54. IEEE (2006)
Chang, H., Yi, M., Harishwaran, H., Abidi, B., Koschan, A., Abidi, M.: Multispectral fusion for indoor and outdoor face authentication. In: 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, pp. 1–6. IEEE (2006)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45344-X_14
Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)
CASIA-FACEV5: Biometrics Ideal Test (2010). http://biometrics.idealtest.org/dbDetailForUser.do?id=9
Bourlai, T., Kalka, N., Ross, A., Cukic, B., Hornak, L.: Cross-spectral face verification in the short wave infrared (SWIR) band. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1343–1347. IEEE (2010)
Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multimed. 12(7), 682–691 (2010)
Maeng, H., Liao, S., Kang, D., Lee, S.-W., Jain, A.K.: Nighttime face recognition at long distance: cross-distance and cross-spectral matching. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7725, pp. 708–721. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37444-9_55
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Fonnegra, R.D., Molina, A., Pérez-Zapata, A.F., Díaz, G.M. (2018). MSpecFace: A Dataset for Facial Recognition in the Visible, Ultra Violet and Infrared Spectra. In: Botto-Tobar, M., Esparza-Cruz, N., León-Acurio, J., Crespo-Torres, N., Beltrán-Mora, M. (eds) Technology Trends. CITT 2017. Communications in Computer and Information Science, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-72727-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-72727-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72726-4
Online ISBN: 978-3-319-72727-1
eBook Packages: Computer ScienceComputer Science (R0)