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Local tri directional pattern (LTDP): a novel descriptor for face recognition in unconstrained conditions

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Abstract

Plentiful of local descriptors has been reported based on Local Binary Pattern (LBP). LBP and most of them establishes a uniform coordination among neighbors and center pixel to develop its code. To be precise the meaning full information located in different directions are missed in the earlier research. In addition the magnitude features are minimal used in earlier research. The invented work develop a novel local descriptor for Face Recognition (FR) called Local Tri Directional Pattern (LTDP) in various unconstrained conditions, by eliminating these problems. LTDP captures direction features from 3 × 3 patch based on first order derivatives generated in clockwise, center and anticlockwise directions, for each neighborhood position of the 3 × 3 patch. The generated first order derivatives are then conceived by novel thresholding function to form the tri directional pattern. The tri directional pattern is further split into three binary patterns, which is further transformed into three LTDP codes by weights assignment and summing values. To increase more discriminativity two magnitude features are also proposed and integrated with the previously extracted features. Eventually all five LTDP codes are merged to develop the size of LTDP for single position. Further all the generated histograms are merged to develop LTDP feature size. Principal Component Analysis (PCA) and Fishers Linear Discriminant Analysis (FLDA) are used for feature compaction and matching is done by Support Vector Machines (SVMs). Results on ORL, GT, EYB and YB illustrates the efficacy of LTDP against the compared methods.

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Data availability

The proposed work don't receive any funds from respective funding organization. This work does not involve humans or animals for experiments evaluation. Experiments are done on datasets whose references are listed in reference section.

References

  1. Qu H, Wang Y (2022) Application of optimized local binary pattern algorithm in small pose face recognition under machine vision. Multimed Tools Appl

  2. Ogla R, Saeid AA, Shaker SH (2022) Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram. Int J Electr Comput Eng 12(3):2571–2581

    Google Scholar 

  3. Kaya Y, Ertugrul OF, Tekin R (2015) Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 34:728–735

    Article  Google Scholar 

  4. Tiwari D, Tyagi V (2016) Dynamic texture recognition based on completed volume local binary pattern. Multidimension Syst Signal Process 27:563–575

    Article  Google Scholar 

  5. Poonia P, Ajmera PK (2022) Palm-print recognition based on quality estimation and feature dimension. Int J Comput Sci Eng 25(2):116–127

    Google Scholar 

  6. Zhao S, Wu J, Fei L, Zhang B, Zhao P (2022) Double-cohesion learning based multiview and discriminant palmprint recognition. Information Fusion 83–84:96–109

    Article  Google Scholar 

  7. Hassaballah M, Alshazly HA, Ali AA (2020) Robust local oriented patterns for ear recognition. Multimedia Tools and Applications 79:31183–31204

    Article  Google Scholar 

  8. Regouid M, Touahria M, Benouis M, Mostefai L, Lamiche I (2022) Comparative study of 1D-local descriptors for ear biometric system. Multimed Tools Appl

  9. Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  ADS  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. Karanwal S, Diwakar M (2021) Neighborhood and center difference-based-LBP for face recognition. Pattern Anal Appl 24:741–761

    Article  Google Scholar 

  12. Karanwal S (2021) COC-LBP: Complete orthogonally combined local binary pattern for face recognition. In: IEEE 12th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

  13. Karanwal S (2021) Improved LBP based descriptors in harsh illumination variations for face recognition. In: International Arab Conference on Information Technology (ACIT)

  14. Hassaballah M, Alshazly HA, Ali AA (2019) Ear recognition using local binary patterns: A comparative experimental study. Expert Syst Appl 118:182–200

    Article  Google Scholar 

  15. Hafiane A, Seetharaman G, Zavidovique B (2007) Median binary patterns for texture classification. In: International Conference on Image Analysis and Recognition (ICIAR), pp 387–398

  16. Karanwal S (2022) Fusion of Two Novel Local Descriptors for Face Recognition in Distinct Challenges. In: 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)

  17. W, Tao D, Li H, Yang Z, Cheng J (2021) Deep features for person re-identification on metric learning. Pattern Recognit 110

  18. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  19. Chen Z, Zhang L, Cao Z, Guo J (2018) Distilling the knowledge from handcrafted features for human activity recognition. IEEE Trans Industr Inf 14(10):4334–4342

    Article  Google Scholar 

  20. Makhmudkhujaev F, Wadud MAA, Iqbal MTB, Ryu B, Chae O (2019) Facial expression recognition with local prominent directional pattern. Signal Process Image Commun 74:1–12

    Article  Google Scholar 

  21. Sharma RP, Dey S (2021) A comparative study of handcrafted local texture descriptors for fingerprint liveness detection under real world scenarios. Multimed Tools Appl 80:9993–10012

    Article  Google Scholar 

  22. Marcolin F, Vezzetti E (2016) Novel descriptors for geometrical 3D face analysis. Multimed Tools Appl

  23. Sghaier S, Farhat W, Souani C (2018) Novel Technique for 3D Face Recognition Using Anthropometric Methodology. Int J Ambient Comput Intell 9(1):60–77

    Article  Google Scholar 

  24. Marcolin F, Vezzetti E, Monaci MG (2021) Face perception foundations for pattern recognition algorithms. Neurocomputing 443:302–319

    Article  Google Scholar 

  25. Ghalleb AEK, Sghaier S, Amara NEB (2013) Face Recognition Improvement Using Soft Biometrics. In: 10th International Multi-Conference SSD'13 1569715537 on Systems, Signals & Devices (SSD), pp 1–6

  26. Kravchik M, Shabtai A (2021) Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA. IEEE Trans Dependable Secure Comput

  27. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  28. Hazarika BB, Gupta D (2021) Density-weighted support vector machines for binary class imbalance learning. Neural Comput Appl 33:4243–4261

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Vu HN, Nguyen MH, Pham C (2022) Masked face recognition with convolutional neural networks and local binary patterns. Appl Intell 52:5497–5512

    Article  Google Scholar 

  31. Zhang Z, Wang M (2022) Multi-feature fusion partitioned local binary pattern method for finger vein recognition. SIViP 16:1091–1099

    Article  Google Scholar 

  32. Khanna K, Gambhir S, Gambhir M (2022) A novel technique for image classification using short-time Fourier transform and local binary pattern. Multimedia Tools and Applications 81:20705–20718

    Article  Google Scholar 

  33. Guo D, Zhang B, Yang S (2021) An improved face recognition algorithm based on extended local binary pattern. In: International Conference on Computer Vision, Image and Deep Learning (ICCVID)

  34. Jiang T, Wang L, Zhao X (2016) Facial expression recognition based on adaptively weighted improved Local Binary Patter. In: International Conference on Digital Image Processing (ICDIP)

  35. Kola DGR, Samayamantula SK (2021) A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimed Tools Appl 80:2243–2262

    Article  Google Scholar 

  36. Liu J, Chen Y, Sun S (2019) A novel local texture feature extraction method called multi-direction local binary pattern. Multimed Tools Appl 78:18735–18750

    Article  Google Scholar 

  37. Shakoor MH, Boostani R (2018) Radial mean local binary pattern for noisy texture classification. Multimed Tools Appl 77:21481–21508

    Article  Google Scholar 

  38. Karanwal S, Diwakar M (2021) MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for Face Recognition. In: Machine Learning, Advances in Computing Renewable Energy and Communication (MARC), pp 613–622

  39. Kral, P, Vrba A, Lenc L (2017) Enhanced Local Binary Patterns for Automatic Face Recognition. arXiv: 1702.03349v2.

  40. Ahuja B, Vishwakarma VP (2020) Local Binary Pattern Based ELM for Face Identification. In: Proceedings of International Conference on Artificial Intelligence and Applications, pp 363–369.

  41. Karanwal S, Diwakar M (2022) NABILD: Noise and Blur Invariant Local Descriptor for Face Recognition. In: International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication and Computational Intelligence (RAEEUCCI)

  42. Wan M, Yao Y, Zhan T, Yang G (2022) Supervised Low-Rank Embedded Regression (SLRER) for Robust Subspace Learning. IEEE Trans Circuits Syst Video Technol 32(4):1917–1927

    Article  Google Scholar 

  43. Wan M, Chen X, Zhan T, Xu C, Yang G, Zhou H (2021) Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction. Inf Sci 563:1–15

    Article  MathSciNet  ADS  Google Scholar 

  44. Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2017) Local graph embedding based on maximum margin criterion via Fuzzy Set. Fuzzy Sets Syst

  45. Nguyen HT, Caplier A (2012) Elliptical Local Binary Patterns for Face recognition. In: Asian Conference on Computer Vision (ACCV), pp 85–96

  46. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Analysis and Modeling of Faces and Gestures, pp 168–182

  47. 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

    Article  Google Scholar 

  48. Goyani M, Patel N (2017) Recognition of Facial Expressions using Local Mean Binary Pattern. Electron Lett Comput Vis Image Anal 16(1):54–67

    Google Scholar 

  49. Zhang Y, Liu W, Fan H, Zou Y, Cui Z, Wang Q (2022) Dictionary learning and face recognition based on sample expansion. Appl Intell 52:3766–3780

    Article  Google Scholar 

  50. Zhang Y, Yan L (2022) A fast face recognition based on image gradient compensation for feature description. Multimed Tools Appl

  51. Karanwal S (2021) A comparative study of 14 state of art descriptors for face recognition. Multimed Tools Appl 80:12195–12234

    Article  Google Scholar 

  52. Lu Y, Khan M, Ansari MD (2022) Face recognition algorithm based on stack denoising and self-encoding LBP. J Intell Syst 31:501–510

    Google Scholar 

  53. Wang X, Shi L, Liu J, Zhang M (2022) Cosine 2DPCA with weighted projection maximization. IEEE Trans Neural Netw Learn Syst

  54. Karanwal S, Diwakar M (2021) Two novel color local descriptors for face recognition. Optik-Int J Light Electron Optics 226(2):1–15

    Google Scholar 

  55. Zeng S, Gou J, Deng L (2017) An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization. Expert Syst Appl 82:1–9

    Article  Google Scholar 

  56. Shao C, Song X, Yang X, Wu X (2016) Extended minimum-squared error algorithm for robust face recognition via auxiliary mirror samples. Soft Comput 20:3177–3187

    Article  Google Scholar 

  57. Qin Y, Sun L, Xu Y (2020) Exploring of alternative representations of facial images for face recognition. Int J Mach Learn Cybern 10

  58. Gou J, Qiu W, Yi Z, Shen X, Zhan Y, Ou W (2019) Locality constrained representation-based K-nearest neighbor classification. Knowl Based Syst 167:38–52

    Article  Google Scholar 

  59. Lu J, Wang H, Zhou J, Chen Y, Lai Z, Hu Q (2021)Low-Rank Adaptive Graph Embedding for Unsupervised Feature Extraction. Pattern Recognit 113

  60. Dornaika F (2022) On the use of high-order feature propagation in Graph Convolution Networks with Manifold Regularization. Inf Sci 584:467–478

    Article  Google Scholar 

  61. Song T, Feng J, Luo L, Gao C, Li H (2021) Robust Texture Description Using Local Grouped Order Pattern and Non-Local Binary Pattern. IEEE Trans Circuits Syst Video Technol 31(1):189–202

    Article  Google Scholar 

  62. Lu C, An S, Liu W, Liu X (2011) An Innovative Weighted 2DLDA Approach for Face Recognition. J Signal Process Syst

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  1. Department of CSE, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India

    Shekhar Karanwal

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Karanwal, S. Local tri directional pattern (LTDP): a novel descriptor for face recognition in unconstrained conditions. Multimed Tools Appl 83, 28419–28441 (2024). https://doi.org/10.1007/s11042-023-16635-9

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