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Local directional relation pattern for unconstrained and robust face retrieval

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Abstract

Face recognition is still a very demanding area of research. This problem becomes more challenging in unconstrained environment and in the presence of several variations like pose, illumination, expression, etc. Local descriptors are widely used for this task. The most of the existing local descriptors consider only few immediate local neighbors and not able to utilize the wider local information to make the descriptor more discriminative. The wider local information based descriptors mainly suffer due to the increased dimensionality. In this paper, this problem is solved by encoding the relationship among directional neighbors in an efficient manner. The relationship between the center pixel and the encoded directional neighbors is utilized further to form the proposed local directional relation pattern (LDRP). The descriptor is inherently uniform illumination invariant. The multi-scale mechanism is also adapted to further boost the discriminative ability of the descriptor. The proposed descriptor is evaluated under the image retrieval framework over face databases. Very challenging databases like PaSC, LFW, PubFig, ESSEX, FERET, AT&T, and FaceScrub are used to test the discriminative ability and robustness of LDRP descriptor. Results are also compared with the recent state-of-the-art face descriptors such as LBP, LTP, LDP, LDN, LVP, DCP, LDGP and LGHP. Very promising performance is observed using the proposed descriptor over very appealing face databases as compared to the existing face descriptors. The proposed LDRP descriptor also outperforms the pre-trained ImageNet CNN models over large-scale FaceScrub face dataset. Moreover, it also outperforms the deep learning based DLib face descriptor in many scenarios.

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  1. http://megaface.cs.washington.edu/participate/challenge.html

References

  1. 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

    Article  MATH  Google Scholar 

  2. Ahonen T, Rahtu E, Ojansivu V, Heikkila J (2008) Recognition of blurred faces using local phase quantization. In: 2008. ICPR 2008. 19th international conference on Pattern recognition. IEEE, pp 1–4

  3. Arandjelovic O (2012) Gradient edge map features for frontal face recognition under extreme illumination changes. In: BMVC 2012: Proceedings of the British machine vision association conference. BMVA Press, pp 1–11

  4. Beveridge JR, Phillips PJ, Bolme DS, Draper BA, Givens GH, Lui YM, Teli MN, Zhang H, Scruggs WT, Bowyer KW et al (2013) The challenge of face recognition from digital point-and-shoot cameras. In: 2013 IEEE Sixth International Conference on Biometrics: theory, Applications and Systems (BTAS). IEEE, pp 1–8

  5. Cao Z, Yin Q, Tang X, Sun J (2010) Face recognition with learning-based descriptor. In: 2010 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp 2707–2714

  6. Chakraborty S, Singh S, Chakraborty P (2016) Local gradient hexa pattern: A descriptor for face recognition and retrieval. IEEE Transactions on Circuits and Systems for Video Technology

  7. Chakraborty S, Singh SK, Chakraborty P (2017) Local directional gradient pattern: a local descriptor for face recognition. Multimed Tools Appl 76(1):1201–1216

    Article  Google Scholar 

  8. Chan CH, Tahir MA, Kittler J, Pietikäinen M (2013) Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans Pattern Anal Mach Intell 35(5):1164–1177

    Article  Google Scholar 

  9. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  10. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009. CVPR 2009. IEEE conference on Computer vision and pattern recognition. IEEE, pp 248–255

  11. Ding L, Ding X, Fang C (2012) Continuous pose normalization for pose-robust face recognition. IEEE Signal Process Lett 19(11):721–724

    Article  Google Scholar 

  12. Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24(3):980–993

    Article  MathSciNet  MATH  Google Scholar 

  13. Ding C, Choi J, Tao D, Davis LS (2016) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell 38(3):518–531

    Article  Google Scholar 

  14. Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol (TIST) 7(3):37

    Google Scholar 

  15. Dlib face descriptor. https://github.com/ageitgey/face_recognition. Accessed: 2019-05-01

  16. Dubey SR, Singh SK, Singh RK (2014) Rotation and illumination invariant interleaved intensity order-based local descriptor. IEEE Trans Image Process 23(12):5323–5333

    Article  MathSciNet  MATH  Google Scholar 

  17. Dubey SR, Singh SK, Singh RK (2015) Local diagonal extrema pattern: a new and efficient feature descriptor for ct image retrieval. IEEE Signal Process Lett 22 (9):1215–1219

    Article  Google Scholar 

  18. Dubey SR, Singh SK, Singh RK (2015) Local wavelet pattern: a new feature descriptor for image retrieval in medical ct databases. IEEE Trans Image Process 24(12):5892–5903

    Article  MathSciNet  MATH  Google Scholar 

  19. Dubey SR, Singh SK, Singh RK (2016) Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J Biomed Health Inf 20 (4):1139–1147

    Article  Google Scholar 

  20. Dubey SR, Singh SK, Singh RK (2016) Multichannel decoded local binary patterns for content-based image retrieval. IEEE Trans Image Process 25(9):4018–4032

    Article  MathSciNet  MATH  Google Scholar 

  21. Elaiwat S, Bennamoun M, Boussaid F, El-sallam A (2014) 3-d face recognition using curvelet local features. IEEE Signal Process Lett 21(2):172–175

    Article  Google Scholar 

  22. Fan KC, Hung TY (2014) A novel local pattern descriptor—local vector pattern in high-order derivative space for face recognition. IEEE Trans Image Process 23 (7):2877–2891

    Article  MathSciNet  MATH  Google Scholar 

  23. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  24. Hong D, Liu W, Su J, Pan Z, Wang G (2015) A novel hierarchical approach for multispectral palmprint recognition. Neurocomputing 151:511–521

    Article  Google Scholar 

  25. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. rep. Technical Report 07-49, University of Massachusetts, Amherst

  26. Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst, Man, Cybern, Part C (Appl Rev) 41 (6):765– 781

    Article  Google Scholar 

  27. Hussain SU, Napoléon T, Jurie F (2012) Face recognition using local quantized patterns. In: British machive vision conference, pp 11

  28. Jabid T, Kabir MH, Chae O (2010) Facial expression recognition using local directional pattern (ldp). In: 2010 17th IEEE international conference on Image processing (ICIP). IEEE, pp 1605–1608

  29. 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

    Article  Google Scholar 

  30. Kemelmacher-Shlizerman I, Seitz SM, Miller D, Brossard E (2016) The megaface benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4873–4882

  31. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  32. Kumar N, Berg AC, Belhumeur PN, Nayar SK (2009) Attribute and simile classifiers for face verification. In: 2009 IEEE 12th international conference on Computer vision. IEEE, pp 365–372

  33. Lei Z, Pietikäinen M, Li SZ (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36(2):289–302

    Article  Google Scholar 

  34. Liao S, Law MW, Chung AC (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    Article  MathSciNet  MATH  Google Scholar 

  35. Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. In: International conference on biometrics. Springer, pp 828–837

  36. Liu L, Long Y, Fieguth PW, Lao S, Zhao G (2014) Brint: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process 23(7):3071–3084

    Article  MathSciNet  MATH  Google Scholar 

  37. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data. In: IJCAI, vol 2015, pp 1617–1623

  38. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  39. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol 30, pp 1266–1272

  40. Lu J, Erin Liong V, Zhou J (2015) Simultaneous local binary feature learning and encoding for face recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3721– 3729

  41. Lu J, Liong VE, Zhou X, Zhou J (2015) Learning compact binary face descriptor for face recognition. IEEE Trans Pattern Anal Mach Intell 37(10):2041–2056

    Article  Google Scholar 

  42. Lu K, He N, Xue J, Dong J, Shao L (2015) Learning view-model joint relevance for 3d object retrieval. IEEE Trans Image Process 24(5):1449–1459

    Article  MathSciNet  MATH  Google Scholar 

  43. Lumini A, Nanni L, Brahnam S (2017) Ensemble of texture descriptors and classifiers for face recognition. Appl Comput Inf 13(1):79–91

    Google Scholar 

  44. Murala S, Maheshwari R, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886

    Article  MathSciNet  MATH  Google Scholar 

  45. Ng HW, Winkler S (2014) A data-driven approach to cleaning large face datasets. In: 2014 IEEE International conference on image processing (ICIP). IEEE, pp 343–347

  46. 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  MATH  Google Scholar 

  47. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  48. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The feret evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22 (10):1090–1104

    Article  Google Scholar 

  49. Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Local binary patterns for still images. In: Computer vision using local binary patterns. Springer, pp 13–47

  50. Punnappurath A, Rajagopalan AN, Taheri S, Chellappa R, Seetharaman G (2015) Face recognition across non-uniform motion blur, illumination, and pose. IEEE Trans Image Process 24 (7):2067– 2082

    Article  MathSciNet  MATH  Google Scholar 

  51. PVSSR CM, et al. (2016) Dimensionality reduced local directional pattern (dr-ldp) for face recognition. Expert Syst Appl 63:66–73

    Article  Google Scholar 

  52. Qi X, Xiao R, Li CG, Qiao Y, Guo J, Tang X (2014) Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans Pattern Anal Mach Intell 36 (11):2199–2213

    Article  Google Scholar 

  53. 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

    Article  Google Scholar 

  54. Rivera AR, Castillo JR, Chae O (2013) Local directional number pattern for face analysis: Face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    Article  MathSciNet  MATH  Google Scholar 

  55. Ryu B, Rivera AR, Kim J, Chae O (2017) Local directional ternary pattern for facial expression recognition. IEEE Transactions on Image Processing

  56. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: 1994., Proceedings of the Second IEEE Workshop on Applications of Computer Vision. IEEE, pp 138–142

  57. Sanderson C, Lovell BC (2009) Multi-region probabilistic histograms for robust and scalable identity inference. In: International conference on biometrics. Springer, pp 199–208

  58. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 815–823

  59. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  60. Spacek L University of essex face database. http://cswww.essex.ac.uk/mv/allfaces/

  61. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  62. Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708

  63. 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

    Article  MathSciNet  MATH  Google Scholar 

  64. Tang H, Yin B, Sun Y, Hu Y (2013) 3d face recognition using local binary patterns. Signal Process 93(8):2190–2198

    Article  Google Scholar 

  65. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, vol 1, pp I–I

  66. Vu NS (2013) Exploring patterns of gradient orientations and magnitudes for face recognition. IEEE Trans Inf Forensic Secur 8(2):295–304

    Article  Google Scholar 

  67. Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990

    Article  Google Scholar 

  68. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  69. Xie S, Shan S, Chen X, Chen J (2010) Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361

    Article  MathSciNet  MATH  Google Scholar 

  70. Yang B, Chen S (2013) A comparative study on local binary pattern (lbp) based face recognition: Lbp histogram versus lbp image. Neurocomputing 120:365–379

    Article  Google Scholar 

  71. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: 2005. ICCV 2005. Tenth IEEE international conference on Computer vision. IEEE, vol 1, pp 786–791

  72. 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

    Article  MathSciNet  Google Scholar 

  73. Zhang X, Gao Y (2009) Face recognition across pose: a review. Pattern Recogn 42(11):2876–2896

    Article  Google Scholar 

  74. Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544

    Article  MathSciNet  MATH  Google Scholar 

  75. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458

    Article  Google Scholar 

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Acknowledgments

This research is funded by IIIT Sri City, India through the Faculty Seed Research Grant.

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  1. Computer Vision Group, Indian Institute of Information Technology, Sri City, Chittoor, Andhra Pradesh, 517646, India

    Shiv Ram Dubey

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Dubey, S.R. Local directional relation pattern for unconstrained and robust face retrieval. Multimed Tools Appl 78, 28063–28088 (2019). https://doi.org/10.1007/s11042-019-07908-3

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