Abstract
A novel methodology for matching of illumination-invariant and heterogeneous faces is proposed here. We present a novel image representation called local extremum logarithm difference (LELD). Theoretical analysis proves that LELD is an illumination-invariant edge feature in coarse level. Since edges are invariant in different modalities, more importance is given on edges. Finally, a novel local zigzag binary pattern LZZBP is presented to capture the local variation of LELD, and we call it a zigzag pattern of local extremum logarithm difference (ZZPLELD). For refinement of ZZPLELD, a model based weight value learning is suggested. We tested the proposed methodology on different illumination variations, sketch-photo and NIR-VIS benchmark databases. Rank-1 recognition of 96.93% on CMU-PIE database and 95.81% on Extended Yale B database under varying illumination, show that ZZPLELD is an efficient method for illumination invariant face recognition. In the case of viewed sketches, the rank-1 recognition accuracy of 98.05% is achieved on CUFSF database. In the case of NIR-VIS matching, the rank-1 accuracy of 99.69% is achieved and which is superior to other state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, S., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using NIR images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)
Li, S.: Encyclopaedia of Biometrics. Springer, Boston (2009)
Tang, X., Wang, X.: Face sketch recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 50–57 (2004)
Chen, J., Yi, D., Yang, J., Zhao, G., Li, S., Pietikainen, M.: Learning mappings for face synthesis from near infrared to visual light images. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 156–163 (2009)
Gao, X., Zhong, J., Li, J., Tian, C.: Face sketch synthesis algorithm on e-hmm and selective ensemble. IEEE Trans. Circ. Syst. Video Technol. 18(4), 487–496 (2008)
Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 1955–1967 (2009)
Li, J., Hao, P., Zhang, C., Dou, M.: Hallucinating faces from thermal infrared images. In: Proceedings of the IEEE International Conference on Image Processing, pp. 465–468 (2008)
Gao, X., Wang, N., Tao, D., Li, X.: Face sketchphoto synthesis and retrieval using sparse representation. IEEE Trans. Circ. Syst. Video Technol. 22(8), 1213–1226 (2012)
Wang, N., Li, J., Tao, D., Li, X., Gao, X.: Heterogeneous image transformation. Elsevier J. Pattern Recogn. Lett. 34, 77–84 (2013)
Wang, N., Tao, D., Gao, X., Li, X., Li, J.: Transductive face sketch-photo synthesis. IEEE Trans. Neural Netw. 24(9), 1364–1376 (2013)
Peng, C., Gao, X., Wang, N., Tao, D., Li, X., Li, J.: Multiple representation-based face sketch-photo synthesis. IEEE Trans. Neural Netw. 27(11), 1–13 (2016)
Lin, D., Tang, X.: Inter-modality face recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 13–26. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_2
Yi, D., Liu, R., Chu, R., Lei, Z., Li, S.: Face matching between near infrared and visible light images. In: Proceedings of International Conference on Biometrics, pp. 523–530 (2007)
Lei, Z., Li, S.: Coupled spectral regression for matching heterogeneous faces. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1123–1128 (2009)
Sharma, A., Jacobs, D.: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 593–600 (2011)
Mignon, A., Jurie, F.: CMML: a new metric learning approach for cross modal matching. In: Proceedings of Asian Conference on Computer Vision, pp. 1–14 (2012)
Lei, Z., Liao, S., Jain, A.K., Li, S.Z.: Coupled discriminant analysis for heterogeneous face recognition. IEEE Trans. Inf. Forensics Secur. 7(6), 1707–1716 (2012)
Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2016)
Liao, S., Yi, D., Lei, Z., Qin, R., Li, S.: Heterogeneous face recognition from local structure of normalized appeaaranceshared representation learning for heterogeneous face recognition. In: Proceedings of IAPR International Conference on Biometrics (2009)
Klare, B.F., Li, Z., Jain, A.K.: Matching forensic sketches to mug shot photos. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 639–646 (2011)
Zhang, W., Wang, X., Tang, X.: Coupled information-theoretic encoding for face photo-sketch recognition. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 513–520 (2011)
Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: Memetically optimized MCWLD for matching sketches with digital face images. IEEE Trans. Inf. Forensics Secur. 7(5), 1522–1535 (2012)
Klare, B.F., Jain, A.K.: Heterogeneous face recognition using kernel prototype similarities. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1410–1422 (2013)
Zhu, J., Zheng, W., Lai, J., Li, S.: Matching NIR face to VIS face using transduction. IEEE Trans. Inf. Forensics Secur. 9(3), 501–514 (2014)
Gong, D., Li, Z., Liu, J., Qiao, Y.: Multi-feature canonical correlation analysis for face photo-sketch image retrieval. In: Proceedings of ACM International Conference on Multimedia, pp. 617–620 (2013)
Roy, H., Bhattacharjee, D.: Heterogeneous face matching using geometric edge-texture feature (getf) and multiple fuzzy-classifier system. Elsevier J. Appl. Soft Comput. 46, 967–979 (2016)
Roy, H., Bhattacharjee, D.: Local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition. IEEE Trans. Inf. Forensics Secur. 11(7), 1412–1424 (2016)
Roy, H., Bhattacharjee, D.: Face sketch-photo matching using the local gradient fuzzy pattern. IEEE J. Intell. Syst. 31(3), 30–39 (2016)
Roy, H., Bhattacharjee, D.: Face sketch-photo recognition using local gradient checksum: LGCS. Springer Int. J. Mach. Learn. Cybern. 8(5), 1457–1469 (2017)
Roy, H., Bhattacharjee, D.: A novel quaternary pattern of local maximum quotient for heterogeneous face recognition. Elsevier Pattern Recogn. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.09.029
Peng, C., Gao, X., Wang, N., Li, J.: Graphical representation for heterogeneous face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 1–13 (2016)
Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.: Face recognition by humans: Nineteen results all computer vision researchers should know about. Proc. IEEE 94, 1948–1962 (2006)
Lai, Z., Dai, D., Ren, C., Huang, K.: Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans. Image Process. 24(6), 1735–1747 (2015)
Ojala, T., Pietikinen, M., Menp, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)
Horn, B.K.P.: Robot Vision. MIT Press, Cambridge (2011)
Zhang, T., Tang, Y.Y., Fang, B., Shang, Z., Liu, X.: Face recognition under varying illumination using gradientfaces. IEEE Trans. Image Process. 18(11), 2599–2606 (2009)
An, G., Wu, J., Ruan, Q.: An illumination normalization model for face recognition under varied lighting conditions. Elsevier J. Pattern Recogn. Lett. 31, 1056–1067 (2010)
Belhumeur, P., Georghiades, A., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Learn. 23(6), 643–660 (2001)
Lee, K.C., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Learn. 27(5), 684–698 (2005)
Li, S.Z., Lei, Z., Ao, M.: The HFB face database for heterogeneous face biometrics research. In: Proceedings of IEEE International Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum, Miami (2009)
Fan, C.N., Zhang, F.Y.: Homomorphic filtering based illumination normalization method for face recognition. Elsevier J. Pattern Recogn. Lett. 32, 1468–1479 (2011)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Lawrence, S., Giles, C.L., Tsoi, A., Back, A.: Face recognition: a convolution neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)
Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Germany
About this chapter
Cite this chapter
Roy, H., Bhattacharjee, D. (2018). A ZigZag Pattern of Local Extremum Logarithm Difference for Illumination-Invariant and Heterogeneous Face Recognition. In: Gavrilova, M., Tan, C., Chaki, N., Saeed, K. (eds) Transactions on Computational Science XXXI. Lecture Notes in Computer Science(), vol 10730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56499-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-56499-8_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-56498-1
Online ISBN: 978-3-662-56499-8
eBook Packages: Computer ScienceComputer Science (R0)