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Pattern Analysis and Applications

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17-02-2023 | Short Paper

Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)

Authors: Subhadeep Koley, Hiranmoy Roy, Soumyadip Dhar, Debotosh Bhattacharjee

Published in: Pattern Analysis and Applications | Issue 3/2023

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Abstract

With the ever-increasing security threats in recent years, biometric authentication has become omnipresent. Among all biometric characteristics, face recognition research has gained traction lately. This paper proposes a new face image descriptor named Local Discrete Cosine Transform Binary Pattern (LDCTBP) for illumination- and modality-invariant face recognition. Utilizing the frequency segregation behavior of Discrete Cosine Transform (DCT), an effective cross-modal illumination-agnostic local feature descriptor has been formulated. Eventually, by encoding the illumination-normalized DCT coefficients into a binary pattern, Local Discrete Cosine Transform Binary Pattern has been generated. Qualitative and quantitative analysis performed on the Extended Yale-B, CUFSF, and TUFTS dataset depict the supremacy of the proposed framework over other state-of-the-arts. Moreover, the proposed LDCTBP has been integrated with a light-weight Convolutional Neural Network (CNN) to prove the importance of handcrafted features in CNN training.

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Koley S, Roy H, Bhattacharjee D (2021) Gammadion binary pattern of Shearlet coefficients (GBPSC): an illumination-invariant heterogeneous face descriptor. Pattern Recognit Lett 145:30–36. https://doi.org/10.1016/j.patrec.2021.01.028CrossRef

Koley S, Roy H, Dhar S, Bhattacharjee D (2022) Illumination invariant face recognition using fused cross lattice pattern of phase congruency (FCLPPC). Inf Sci 584:633–648. https://doi.org/10.1016/j.ins.2021.10.059CrossRef

Roy H, Bhattacharjee D (2016) Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system. Appl Soft Comput J 46:967–979. https://doi.org/10.1016/j.asoc.2015.12.006CrossRef

Bhattacharjee D, Roy H (2021) Pattern of local gravitational force (PLGF): a novel local image descriptor. IEEE Trans Pattern Anal Mach Intell 43(2):595–607. https://doi.org/10.1109/TPAMI.2019.2930192CrossRef

Roy H, Koley S (2021) Local-friis-radiation-pattern (LFRP) for face recognition. Sens Imaging. https://doi.org/10.1007/s11220-020-00325-zCrossRef

Bhattacharya S, Nainala GS, Rooj S, Routray A (2019) Local force pattern (LFP): descriptor for heterogeneous face recognition. Pattern Recognit Lett 125:63–70. https://doi.org/10.1016/j.patrec.2019.03.028CrossRef

Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194. https://doi.org/10.1109/TPAMI.2015.2435740CrossRef

Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35(6):1410–1422. https://doi.org/10.1109/TPAMI.2012.229CrossRef

Liu X, Song L, Wu X, Tan T (2016) Transferring deep representation for NIR-VIS heterogeneous face recognition. In: Proceeding international conference on biometrics, pp 1–8. https://doi.org/10.1109/ICB.2016.7550064

10.

Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. Proc Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2014.220CrossRef

11.

Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) SphereFace: deep hypersphere embedding for face recognition. Proc Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2017.713CrossRef

12.

Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) CosFace: large margin cosine loss for deep face recognition. Proc Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2018.00552CrossRef

13.

Deng J, Guo J, Xue N, Zafeiriou S (2019) ArcFace: additive angular margin loss for deep face recognition. Proc Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2019.00482CrossRef

14.

Goel T, Murugan R (2020) Classifier for face recognition based on deep convolutional - optimized kernel extreme learning machine. Comput Electr Eng 85:106640. https://doi.org/10.1016/j.compeleceng.2020.106640CrossRef

15.

Hu G, Yang Y, Yi D, Kittler J, Christmas W, Li SZ, Hospedales T (2016) When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: Proceedings of the IEEE international conference on computer vision, p 384–392. https://doi.org/10.1109/ICCVW.2015.58

16.

Saeed U, Masood K, Dawood H (2021) Illumination normalization techniques for makeup-invariant face recognition. Comput Electr Eng 89:106921. https://doi.org/10.1016/j.compeleceng.2020.106921CrossRef

17.

Maani R, Kalra S, Yang YH (2013) Rotation invariant local frequency descriptors for texture classification. IEEE Trans Image Process 22(6):2409–2419. https://doi.org/10.1109/TIP.2013.2249081MathSciNetCrossRefMATH

18.

Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623CrossRefMATH

19.

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. https://doi.org/10.1109/TIP.2010.2042645MathSciNetCrossRefMATH

20.

Liu L, Zhao L, Long Y, Kuang G, Fieguth P (2012) Extended local binary patterns for texture classification. Image Vis Comput 30(2):86–99. https://doi.org/10.1016/j.imavis.2012.01.001CrossRef

21.

Ul Hussain S, Napoléon T, Jurie F (2012) Face recognition using local quantized patterns. In Proceeding british machive vision conference, p 716–729. https://doi.org/10.5244/C.26.99

22.

Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. Lect Notes Comput Sci 4642:828–837. https://doi.org/10.1007/978-3-540-74549-5_87CrossRef

23.

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 Part B Cybern 36(2):458–466. https://doi.org/10.1109/TSMCB.2005.857353CrossRef

24.

25.

Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Trans Comput C–23(1):90–93. https://doi.org/10.1109/T-C.1974.223784MathSciNetCrossRefMATH

26.

Roy H, Bhattacharjee D (2016) Local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition. IEEE Trans Inf Forensics Secur 11(7):1412–1424. https://doi.org/10.1109/TIFS.2016.2530043CrossRef

27.

Burt P, Adelson E (1983) The laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540. https://doi.org/10.1109/TCOM.1983.1095851CrossRef

28.

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. https://doi.org/10.1109/34.927464CrossRef

29.

Sim T, Baker S, Bsat M (2003) The cmu pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618. https://doi.org/10.1109/TPAMI.2003.1251154CrossRef

30.

Weyrauch B, Heisele B, Huang J, Blanz V (2004) Component-based face recognition with 3d morphable models. In: Proceedings computer vision pattern recognition workshop, pp 85–85. https://doi.org/10.1109/CVPR.2004.315

31.

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings computer vision pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

32.

Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967. https://doi.org/10.1109/TPAMI.2008.222CrossRef

33.

Zhang W, Wang X, Tang X (2011) Coupled information-theoretic encoding for face photo-sketch recognition. In: Proceedings computer vision pattern recognition, pp 513–520. https://doi.org/10.1109/CVPR.2011.5995324

34.

Panetta K, Samani A, Yuan X, Wan Q, Agaian S, Rajeev S, Kamath S, Rajendran R, Rao SP, Kaszowska A, Taylor HA (2020) A comprehensive database for benchmarking imaging systems. IEEE Trans Pattern Anal Mach Intell 42(3):509–520. https://doi.org/10.1109/TPAMI.2018.2884458CrossRef

35.

Roy H, Bhattacharjee D (2017) Face sketch-photo recognition using local gradient checksum: LGCS. Int J Mach Learn Cybern 8(5):1457–1469. https://doi.org/10.1007/s13042-016-0516-0CrossRef

36.

Roy H, Bhattacharjee D (2016) Face sketch-photo matching using the local gradient fuzzy pattern. IEEE Intell Syst 31(3):30–39. https://doi.org/10.1109/MIS.2016.44CrossRef

37.

Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384. https://doi.org/10.1016/j.neucom.2013.03.020CrossRef

38.

Lai ZR, Dai DQ, Ren CX, Huang KK (2015) Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans Image Process 24(6):1735–1747. https://doi.org/10.1109/TIP.2015.2409988MathSciNetCrossRefMATH

39.

Seo HJ, Milanfar P (2011) Face verification using the lark representation. IEEE Trans Inf Forensics Secur 6(4):1275–1286. https://doi.org/10.1109/TIFS.2011.2159205CrossRef

40.

An G, Wu J, Ruan Q (2010) An illumination normalization model for face recognition under varied lighting conditions. Pattern Recognit Lett 31(9):1056–1067. https://doi.org/10.1016/j.patrec.2010.01.021CrossRef

41.

Lu J, Liong VE, Zhou J (2018) Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition. IEEE Trans Pattern Anal Mach Intell 40(8):1979–1993. https://doi.org/10.1109/TPAMI.2017.2737538CrossRef

42.

Cao B, Wang N, Li J, Gao X (2019) Data augmentation-based joint learning for heterogeneous face recognition. IEEE Trans Neural Netw Learn Syst 30(6):1731–1743. https://doi.org/10.1109/TNNLS.2018.2872675MathSciNetCrossRef

43.

Wang N, Gao X, Li J (2018) Random sampling for fast face sketch synthesis. Pattern Recognit 76:215–227. https://doi.org/10.1016/j.patcog.2017.11.008CrossRef

44.

Wang N, Gao X, Sun L, Li J (2018) Anchored neighborhood index for face sketch synthesis. IEEE Trans Circuits Syst Video Technol 28(9):2154–2163. https://doi.org/10.1109/TCSVT.2017.2709465CrossRef

45.

Ren CX, Lei Z, Dai DQ, Li SZ (2016) Enhanced local gradient order features and discriminant analysis for face recognition. IEEE Trans Cybern 46(11):2656–2669. https://doi.org/10.1109/TCYB.2015.2484356CrossRef

46.

Pang M, Ming Cheung Y, Wang B, Liu R (2019) Robust heterogeneous discriminative analysis for face recognition with single sample per person. Pattern Recognit 89:91–107. https://doi.org/10.1016/j.patcog.2019.01.005CrossRef

47.

Roy H, Bhattacharjee D (2018) A novel quaternary pattern of local maximum quotient for heterogeneous face recognition. Pattern Recognit Lett 113:19–28. https://doi.org/10.1016/j.patrec.2017.09.029CrossRef

48.

Roy H, Bhattacharjee D (2018) A novel local wavelet energy mesh pattern (LWEMeP) for heterogeneous face recognition. Image Vis Comput 72:1–13. https://doi.org/10.1016/j.imavis.2018.01.004CrossRef

Title: Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)
Authors: Subhadeep Koley
Hiranmoy Roy
Soumyadip Dhar
Debotosh Bhattacharjee
Publication date: 17-02-2023
Publisher: Springer London
Published in: Pattern Analysis and Applications / Issue 3/2023
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-023-01139-x

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