Abstract
In this paper, we propose an octagonal prism representation for local binary patterns (LBP). This representation implements a new circular distance measurement for face recognition under various illumination conditions. The LBP method has been widely used in many computer vision applications, particularly for face recognition. Most LBP matching methods use distribution features with a bin-to-bin distance measure. However, using this bin-to-bin distance measure may produce low similarity scores even for similar patterns. To address this problem, we placed the LBPs on an octagonal prism in a three dimensional space and used the Euclidean distance measure. In the proposed octagonal prism representation, the LBPs were represented as three dimensional vectors on the octagonal prism. Since similar patterns under different illumination conditions are located in the vicinity on the octagonal prism, the proposed method proved robust against illumination variations. The proposed method produced noticeably improved performance when using the CMU PIE, Yale B, and Extended Yale B databases.
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References
Adini Y, Moses Y, Ullman S (1997) Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell 19(7):721–732
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Ali W, Georgsson F, Hellstrom T (2008) Visual tree detection for autonomous navigation in forest environment. In: Proc. IEEE Intell. Veh. Symp., pp 560–565
Basri R, Jacobs D (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233
Chen T, Yin W, Zhou XS, Comaniciu D, Huang TS (2006) Total variation models for variable lighting face recognition. IEEE Trans Pattern Anal Mach Intell 28(9):1519–1524
Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cybern B Cybern 36(2):458–466
Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660
Grangier D, Bengio S (2008) A discriminative kernel-based approach to rank images from text queries. IEEE Trans Pattern Anal Mach Intell 30(8):1371–1384
Han H, Shan S, Chen X, Gao W (2013) A comparative study on illumination preprocessing in face recognition. Pattern Recogn 46(6):1691–1699
Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662
Huijsmans DP, Sebe N (2003) Content-based indexing performance: a class size normalized precision, recall, generality evaluation. In: Proc. Int. Conf. Image Process., pp 733–736
Kellokumpu V, Zhao G, Pietikainen M (2008) Human activity recognition using a dynamic texture based method. presented at the Brit. Mach. Vis. Conf
Kluckner S, Pacher G, Grabner H, Bischof H, Bauer J (2007) A 3D teacher for car detection in aerial images. In: Proc. IEEE Int. Conf. Comput. Vis., pp 1–8
Le KN (2011) A mathematical approach to edge detection in hyperbolic distributed and Gaussian-distributed pixel-intensity images using hyperbolic and Gaussian masks. Digit Signal Process 21(1):162–181
Le KN, Dabke KP, Egan GK (2006) On mathematical derivations of auto-term functions and signal-to-noise ratios of Choi–Williams, first and nth-order hyperbolic kernels. Digit Signal Process 16(1):84–104
Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698
Lee PH, Wu SW, Hung YP (2012) Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Trans Image Process 21(9):4280–4289
Li Z, Liu G, Yang Y, You J (2012) Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Trans Image Process 21(4):2130–2140
Liu HD, Yang M, Gao Y, Cui C (2014) Local histogram specification for face recognition under varying lighting conditions. Image Vis Comput 32(5):335–347
Liu L, Fieguth P, Zhao G, Pietikäinen M, Hu D (2016) Extended local binary patterns for face recognition. Inf Sci 358:56–72
Lucieer A, Stein A, Fisher P (2005) Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. Int J Remote Sens 26(14):2917–2936
Maenpaa T, Viertola J, Pietikainen M (2003) Optimising colour and texture features for real-time visual inspection. Pattern Anal Applic 6(3):169–175
Nanni L, Lumini A (2008) Ensemble of multiple pedestrian representations. IEEE Trans Intell Transp Syst 9(2):365–369
Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distribution. Pattern Recogn 29(1):51–59
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Oliver A, Lladó X, Freixenet J, Martí J (2007) False positive reduction in mammographic mass detection using local binary patterns. In: Proc. Med. Image Comput. Comput. Assisted Intervention Conf., pp 286–293
Pang Y, Yuan Y, Li X (2008) Gabor-based region covariance matrices for face recognition. IEEE Trans Circuits Syst Video Technol 18(7):989–993
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. BMVC 1(3):1–12
Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368
Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. In: Proceedings of the ICCV Workshop on Analysis and Modeling of Faces and Gestures, pp 157–164
Sim T, Baker S, Bsat M (2001) The CMU pose, illumination, and expression (PIE) database of human faces. Carnegie Mellon Univ., Pittsburgh Tech. Rep. CMU-RI-TR-01-02
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Turtinen M, Pietikainen M, Silven O (2006) Visual characterization of paper using Isomap and local binary patterns. IEICE Trans Inf Syst E89-D(7):2076–2083
Wang H, Li SZ, Wang Y (2004) Face recognition under varying lighting conditions using self quotient image. In: Proceedings of the Automatic Face and Gesture Recognition, pp 819–824
Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on Weber’s law with application to face recognition. Signal Process Lett 18(8):462–465
Wiskott L, Fellous J-M, Kruger N, von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779
Wu Y, Jiang Y, Zhou Y, Li W, Lu Z, Liao Q (2014) Generalized Weber-face for illumination-robust face recognition. Neurocomputing 136:262–267
Xie X, Zheng W-S, Lai J, Yuen P (2008) Face illumination normalization on large and small scale features. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 1–8
Zhang L, Samaras D (2006) Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans Pattern Anal Mach Intell 28(3):351–363
Zhang B, Shan S, Chen X, Gao W (2007) Histogram of gabor phase patterns (hgpp): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68
Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. In: Proc. 1st IEEE Int. Conf. Biometrics: Theory, Appl., Syst., pp 1–8
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A01006421).
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Lee, K., Jeong, T., Woo, S. et al. Octagonal prism LBP representation for face recognition. Multimed Tools Appl 77, 21751–21770 (2018). https://doi.org/10.1007/s11042-017-5583-z
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DOI: https://doi.org/10.1007/s11042-017-5583-z