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Eccentricity based kinship verification from facial images in the wild

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Abstract

Kinship verification from facial images in the wild is a promising research aiming to identify whether a facial image pair shares kinship relation by analyzing face structures. This paper proposes a novel eccentricity-based kinship verification (EKV) method to demonstrate efficacy of dominant facial sections for kinship verification. The proposed EKV method uses eccentricity of ellipse-approximated dominant facial sections as discriminative parameter to perform kinship verification. It presents two different schemes, namely single eccentricity (SE) and fused eccentricity (FE). SE scheme for EKV method employs single formulation by considering single facial section. Each selected facial section is approximated as an ellipse to compute eccentricity parameter and perform verification. Next, FE scheme for EKV method employs multiview formulation by analyzing two or more facial sections. Eccentricity of different ellipse-approximated facial sections is computed and fused to form a transformed parameter and perform verification. The proposed EKV method is demonstrated on different available kinship databases. Experimental results showcase effectiveness of EKV method with the best and competitive accuracy obtained for FE scheme on different databases.

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Notes

  1. Symbol \(*\) is used for methods using external images for training. Further, symbol # is used for methods based on facial section structures.

References

  1. Kaminski G, Dridi S, Graff C, Gentaz E (2009) Human ability to detect kinship in strangers’ faces: effects of the degree of relatedness. Proc Royal Soc Lond B: Biol Sci 276(1670):3193–3200

    Google Scholar 

  2. Goyal A, Meenpal T (2019) Kinship verification from facial images using feature descriptors. In: Mallick PK, Balas VE, Bhoi Ak, Zobaa AF (eds) Cognitive informatics and soft computing. Springer, Singapore, pp 371–380

    Google Scholar 

  3. Yadav N, Goyal A, Meenpal T (2019) A feature averaging method for kinship verification. In: Mallick PK, Balas VE, Bhoi Ak, Zobaa AF (eds) Cognitive informatics and soft computing. Springer, Singapore, pp 381–391

    Google Scholar 

  4. Robinson JP, Shao M, Wu Y, Liu H, Gillis T, Fu Y (2018) Visual kinship recognition of families in the wild. IEEE Trans Pattern Anal Mach Intell 40(11):2624–2637

    Google Scholar 

  5. Daly M, Wilson MI (1982) Whom are newborn babies said to resemble? Ethol Sociobiol 3(2):69–78

    Google Scholar 

  6. Dal Martello MF, Maloney LT (2010) Lateralization of kin recognition signals in the human face. J Vis 10(8):9–9

    Google Scholar 

  7. Shepherd JW (1981) Studies of cue saliency. Perceiving Rememb Faces, pp 105–131

  8. Dal Martello MF, Maloney LT (2006) Where are kin recognition signals in the human face? J Vis 6(12):2–2

    Google Scholar 

  9. Fornaciari M, Prati A, Cucchiara R (2014) A fast and effective ellipse detector for embedded vision applications. Pattern Recognit 47(11):3693–3708

    Google Scholar 

  10. Cakir HI, Benligiray B, Topal C (2016) Combining feature-based and model-based approaches for robust ellipse detection. In: 2016 24th European signal processing conference (EUSIPCO), pp 2430–2434. IEEE

  11. Prasad DK, Leung MK, Quek C (2013) Ellifit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method. Pattern Recognit 46(5):1449–1465

    MATH  Google Scholar 

  12. Zhang W, Zhang TN, Chang SJ (2011) Eye gaze estimation from the elliptical features of one iris. Opt Eng 50(4):047003

    Google Scholar 

  13. Bai X, Sun C, Zhou F (2009) Splitting touching cells based on concave points and ellipse fitting. Pattern Recognit 42(11):2434–2446

    MATH  Google Scholar 

  14. Zafari S, Eerola T, Sampo J, Kälviäinen H, Haario H (2015) Segmentation of overlapping elliptical objects in silhouette images. IEEE Trans Image Process 24(12):5942–5952

    MathSciNet  MATH  Google Scholar 

  15. Salehian SSM, Khoramshahi M, Billard A (2016) A dynamical system approach for softly catching a flying object: theory and experiment. IEEE Trans Robot 32(2):462–471

    Google Scholar 

  16. Ono K, Ogawa T, Maeda Y, Nakatani S, Nagayasu G, Shimizu R, Ouchi N (2014) Detection, localization and picking up of coil springs from a pile. In: 2014 IEEE International conference on robotics and automation (ICRA), pp 3477–3482. IEEE

  17. Zhou X, Hu J, Lu J, Shang Y, Guan Y (2011) Kinship verification from facial images under uncontrolled conditions. In: Proceedings of the 19th ACM international conference on Multimedia, pp 953–956. ACM

  18. Guo G, Wang X (2012) Kinship measurement on salient facial features. IEEE Trans Instrum Meas 61(8):2322–2325

    Google Scholar 

  19. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Google Scholar 

  20. Mahpod S, Keller Y (2018) Kinship verification using multiview hybrid distance learning. Comput Vis Image Underst 167:28–36

    Google Scholar 

  21. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on vol 1, pp 886–893. IEEE

  22. Fang R, Tang KD, Snavely N, Chen T (2010) Towards computational models of kinship verification. Image processing (ICIP). In: 2010 17th IEEE international conference on, pp 1577–1580. IEEE

  23. Yan H, Lu J, Zhou X (2015) Prototype-based discriminative feature learning for kinship verification. IEEE Trans Cybern 45(11):2535–2545

    Google Scholar 

  24. Qin X, Tan X, Chen S (2015) Tri-subject kinship verification: understanding the core of a family. IEEE Trans Multimed 17(10):1855–1867

    Google Scholar 

  25. Liu Q, Puthenputhussery A, Liu C (2015) Inheritable fisher vector feature for kinship verification. Biometrics theory, applications and systems (BTAS). In: 2015 IEEE 7th international conference on, pp 1–6. IEEE

  26. Cui L, Ma B (2017) Adaptive feature selection for kinship verification. Multimedia and expo (ICME). In: 2017 IEEE international conference on, pp 751–756. IEEE

  27. Alirezazadeha P, Fathia A, Abdali-Mohammadia F (2018) Effect of purposeful feature extraction in high-dimensional kinship verification problem. J Comput Secur 3(3):183–191

    Google Scholar 

  28. Yan H (2019) Learning discriminative compact binary face descriptor for kinship verification. Pattern Recognit Lett 117:146–152

    Google Scholar 

  29. Aliradi R, Belkhir A, Ouamane A, Elmaghraby AS (2018) Dieda: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5572-2

    Article  Google Scholar 

  30. Moujahid A, Dornaika F (2019) A pyramid multi-level face descriptor: application to kinship verification. Multimed Tools Appl 78(7):9335–9354

    Google Scholar 

  31. Xia S, Shao M, Luo J, Fu Y (2012) Understanding kin relationships in a photo. IEEE Trans Multimed 14(4):1046–1056

    Google Scholar 

  32. Lu J, Zhou X, Tan YP, Shang Y, Zhou J (2014) Neighborhood repulsed metric learning for kinship verification. IEEE Trans Pattern Anal Mach Intell 36(2):331–345

    Google Scholar 

  33. Yan H, Lu J, Deng W, Zhou X (2014) Discriminative multimetric learning for kinship verification. IEEE Trans Inform Forensics Secur 9(7):1169–1178

    Google Scholar 

  34. Zhou X, Shang Y, Yan H, Guo G (2016) Ensemble similarity learning for kinship verification from facial images in the wild. Inform Fusion 32:40–48

    Google Scholar 

  35. Xu M, Shang Y (2016) Kinship measurement on face images by structured similarity fusion. IEEE Access 4:10280–10287

    Google Scholar 

  36. Zhou X, Yan H, Shang Y (2016) Kinship verification from facial images by scalable similarity fusion. Neurocomputing 197:136–142

    Google Scholar 

  37. Hu J, Lu J, Tan YP, Yuan J, Zhou J (2017) Local large-margin multi-metric learning for face and kinship verification. IEEE Trans Circuits Syst Video Technol 28:1875–1891

    Google Scholar 

  38. Patel B, Maheshwari R, Raman B (2017) Evaluation of periocular features for kinship verification in the wild. Comput Vis Image Underst 160:24–35

    Google Scholar 

  39. Qin X, Liu D, Wang D (2017) Heterogeneous similarity learning for more practical kinship verification. Neural Process Lett 47:1–17

    Google Scholar 

  40. Zhao YG, Song Z, Zheng F, Shao L (2018) Learning a multiple kernel similarity metric for kinship verification. Inform Sci 430:247–260

    MathSciNet  Google Scholar 

  41. Liang J, Hu Q, Dang C, Zuo W (2018) Weighted graph embedding-based metric learning for kinship verification. IEEE Trans Image Process 28:1–1

    MathSciNet  Google Scholar 

  42. Wei Z, Xu M, Geng L, Liu H, Yin H (2019) Adversarial similarity metric learning for kinship verification. IEEE Access 7:100029–100035

    Google Scholar 

  43. Zhang K, Huang Y, Song C, Wu H, Wang L (September 2015) Kinship verification with deep convolutional neural networks. In: Proceedings of the british machine vision conference (BMVC). BMVA Press, pp 148.1–148.12

  44. Wang M, Li Z, Shu X, Tang J, et al. (2015) Deep kinship verification. Multimedia signal processing (MMSP). In: 2015 IEEE 17th international workshop on, pp 1–6. IEEE

  45. Kohli N, Vatsa M, Singh R, Noore A, Majumdar A (2017) Hierarchical representation learning for kinship verification. IEEE Trans Image Process 26(1):289–302

    MathSciNet  MATH  Google Scholar 

  46. Lu J, Hu J, Tan YP (2017) Discriminative deep metric learning for face and kinship verification. IEEE Trans Image Process 26(9):4269–4282

    MathSciNet  MATH  Google Scholar 

  47. Duan Q, Zhang L (2017) Advnet: Adversarial contrastive residual net for 1 million kinship recognition. In: Proceedings of the 2017 workshop on recognizing families in the wild, pp 21–29. ACM

  48. Yang Y, Wu Q (2017) A novel kinship verification method based on deep transfer learning and feature nonlinear mapping. DEStech Trans Comput Sci Eng (aiea)

  49. Wang S, Ding Z, Fu Y (2018) Cross-generation kinship verification with sparse discriminative metric. IEEE Trans Pattern Anal Mach Intell 41:2783–2790

    Google Scholar 

  50. Tidjani A, Taleb-Ahmed A, Samai D, Eddine AK (2018) Deep learning features for robust facial kinship verification. IET Image Process 12(12):2336–2345

    Google Scholar 

  51. Zhou X, Jin K, Xu M, Guo G (2019) Learning deep compact similarity metric for kinship verification from face images. Inform Fusion 48:84–94

    Google Scholar 

  52. Laiadi O, Ouamane A, Benakcha A, Taleb-Ahmed A, Hadid A (2019) Kinship verification based deep and tensor features through extreme learning machine. In: 2019 14th IEEE international conference on automatic face and gesture recognition (FG 2019), pp 1–4. IEEE

  53. Yan H, Wang S (2019) Learning part-aware attention networks for kinship verification. Pattern Recognit Lett 128:169–175

    Google Scholar 

  54. Dehshibi MM, Shanbehzadeh J (2019) Cubic norm and kernel-based bi-directional pca: toward age-aware facial kinship verification. Vis Comput 35(1):23–40

    Google Scholar 

  55. Zhang L, Duan Q, Zhang D, Jia W, Wang X (2020) Advkin: Adversarial convolutional network for kinship verification. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2959403

    Article  Google Scholar 

  56. Robinson JP, Shao M, Wu Y, Fu Y (2016) Families in the wild (fiw): Large-scale kinship image database and benchmarks. In: Proceedings of the 24th ACM international conference on Multimedia, pp 242–246. ACM

  57. Lopez MB, Hadid A, Boutellaa E, Goncalves J, Kostakos V, Hosio S (2018) Kinship verification from facial images and videos: human versus machine. Mach Vis Appl 29(5):873–890

    Google Scholar 

  58. Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476–480

    Google Scholar 

  59. Chernov N, Huang Q, Ma H (2014) Fitting quadratic curves to data points. Br J Math Comput Sci 4(1):33–60

    Google Scholar 

  60. Bookstein FL (1979) Fitting conic sections to scattered data. Comput Graph Image Process 9(1):56–71

    Google Scholar 

  61. Gander W, Golub GH, Strebel R (1994) Least-squares fitting of circles and ellipses. BIT Numer Math 34(4):558–578

    MathSciNet  MATH  Google Scholar 

  62. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. in computer vision and pattern recognition, 2001. CVPR 2001. In: Proceedings of the 2001 IEEE computer society conference on, vol 1, pp I–I. IEEE

  63. Wu S, Kan M, Shan S, Chen X (2019) Hierarchical attention for part-aware face detection. Int J Comput Vis 127(6–7):560–578

    Google Scholar 

  64. Zhao ZQ, Zheng P, Xu St, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Google Scholar 

  65. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell (6):679–698

  66. Kokare M, Biswas PK, Chatterji BN (2007) Texture image retrieval using rotated wavelet filters. Pattern Recognit Lett 28(10):1240–1249

    Google Scholar 

  67. 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: 10th IEEE international conference on computer vision (ICCV’05), vol 1, pp 786–791. IEEE

  68. Lu J, Hu J, Liong VE, Zhou X, Bottino A, Islam IU, Vieira TF, Qin X, Tan X, Chen S, Mahpod S, Keller Y, Zheng L, Idrissi K, Garcia C, Duffner S, Baskurt A, Castrilln-Santana M, Lorenzo-Navarro J (2015) The fg 2015 kinship verification in the wild evaluation. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), vol 1, pp 1–7

  69. Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. City 1(2):1

    MathSciNet  Google Scholar 

  70. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 12:2037–2041

    MATH  Google Scholar 

  71. 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

    MathSciNet  MATH  Google Scholar 

  72. Wolf L, Hassner T, Taigman Y (2008) Descriptor based methods in the wild. In: Real-life images workshop at the European conference on computer vision (ECCV). https://osnathassner.github.io/talhassner/projects/Patchlbp

  73. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2009) Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Google Scholar 

  74. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing. Springer, Berlin, pp 236–243

  75. Liu C (2004) Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Machine Intell 26(5):572–581

    Google Scholar 

  76. Chen C, Zhang J (2007) Wavelet energy entropy as a new feature extractor for face recognition. Image and graphics, 2007. ICIG 2007. In: 4th International conference on pp 616–619. IEEE

  77. Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: Xie X, Jones MW, Tam GKL (eds) Proceedings of the British machine vision conference (BMVC). BMVA Press, pp 41.1–41.12. https://doi.org/10.5244/C.29.41

  78. Kannala J, Rahtu E (2012) Bsif: binarized statistical image features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 1363–1366. IEEE

  79. Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM international conference on Image and video retrieval, pp 401–408

  80. Thung KH, Raveendran P (2009) A survey of image quality measures. In: 2009 international conference for technical postgraduates (TECHPOS), pp 1–4. IEEE

  81. Alirezazadeh P, Fathi A, Abdali-Mohammadi F (2015) A genetic algorithm-based feature selection for kinship verification. IEEE Signal Process Lett 22(12):2459–2463

    Google Scholar 

  82. Duan X, Tan ZH (2015) A feature subtraction method for image based kinship verification under uncontrolled environments. In: 2015 IEEE international conference on image processing (ICIP), pp 1573–1577. IEEE

  83. Chen X, An L, Yang S, Wu W (2017) Kinship verification in multi-linear coherent spaces. Multimed Tools Appl 76(3):4105–4122

    Google Scholar 

  84. Puthenputhussery A, Liu Q, Liu C (Sept 2016) Sift flow based genetic fisher vector feature for kinship verification. In: 2016 IEEE international conference on image processing (ICIP), pp 2921–2925

  85. Alirezazadeh P, Fathi A, Abdali-Mohammadi F (2018) Effect of purposeful feature extraction in high-dimensional kinship verification problem. J Comput Secur 3:183–191

    Google Scholar 

  86. Dehghan A, Ortiz EG, Villegas R, Shah M (2014) Who do i look like? determining parent-offspring resemblance via gated autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1757–1764

  87. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: 2012 IEEE conference on computer vision and pattern recognition, pp 2160–2167. IEEE

  88. Lu J, Tan YP, Wang G (2012) Discriminative multimanifold analysis for face recognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell 35(1):39–51

    Google Scholar 

  89. Kan M, Shan S, Xu D, Chen X (2011) Side-information based linear discriminant analysis for face recognition. BMVC 11:1–12

    Google Scholar 

  90. Liu H, Zhu C (2017) Status-aware projection metric learning for kinship verification. In: Multimedia and expo (ICME), 2017 IEEE international conference on, pp 319–324. IEEE

  91. Xu M, Shang Y (2016) Kinship verification using facial images by robust similarity learning. Math Probl Eng 2016:4072323

    Google Scholar 

  92. Ding Z, Shao M, Hwang W, Suh S, Han JJ, Choi C, Fu Y (2018) Robust discriminative metric learning for image representation. IEEE Trans Circuits Syst Video Technol 29(11):3173–3183

    Google Scholar 

  93. Li L, Feng X, Wu X, Xia Z, Hadid A (2016) Kinship verification from faces via similarity metric based convolutional neural network. In: International conference on image analysis and recognition. Springer, Berlin, pp 539–548

  94. Laiadi O, Ouamane A, Benakcha A, Taleb-Ahmed A, Hadid A (2020) Multi-view deep features for robust facial kinship verification. arXiv preprint arXiv:2006.01315

  95. Zhang Z, Chen Y, Saligrama V (2015) Group membership prediction. In: Proceedings of the IEEE international conference on computer vision, pp 3916–3924

  96. Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision. Springer, Berlin, pp 499–515

  97. Robinson JP, Shao M, Zhao H, Wu Y, Gillis T, Fu Y (2017) Recognizing families in the wild (rfiw) data challenge workshop in conjunction with acm mm 2017. In: Proceedings of the 2017 workshop on recognizing families in the wild, pp 5–12

  98. Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 212–220

  99. Li Y, Zeng J, Zhang J, Dai A, Kan M, Shan S, Chen X (2017) Kinnet: Fine-to-coarse deep metric learning for kinship verification. In: Proceedings of the 2017 workshop on recognizing families in the wild, pp 13–20

  100. Dawson M, Zisserman A, Nellåker C (2018) From same photo: cheating on visual kinship challenges. In: Asian conference on computer vision. Springer, Berlin, pp 654–668

  101. Hörmann S, Knoche M, Rigoll G (2020) A multi-task comparator framework for kinship verification. arXiv preprint arXiv:2006.01615

  102. Minaee S, Abdolrashidi A, Su H, Bennamoun M, Zhang D (2019) Biometric recognition using deep learning: a survey. arXiv preprint arXiv:1912.00271

  103. 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

  104. Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A (2018) Vggface2: A dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face and gesture recognition (FG 2018), pp 67–74. IEEE

  105. Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: Face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873

  106. Minaee S, Abdolrashidi A, Wang Y (2015) Iris recognition using scattering transform and textural features. In: 2015 IEEE signal processing and signal processing education workshop (SP/SPE), pp 37–42. IEEE

  107. Minaee S, Abdolrashidi A, Wang Y (2017) Face recognition using scattering convolutional network. In: 2017 IEEE signal processing in medicine and biology symposium (SPMB), pp 1–6. IEEE

  108. Minaee S, Wang Y (2017) Palmprint recognition using deep scattering network. In: 2017 IEEE international symposium on circuits and systems (ISCAS), pp 1–4. IEEE

  109. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Google Scholar 

  110. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Google Scholar 

  111. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Google Scholar 

  112. 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 318:120–131

    MathSciNet  Google Scholar 

  113. Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Inform Sci 274:55–69

    Google Scholar 

  114. Wan M, Yang G, Gai S, Yang Z (2017) Two-dimensional discriminant locality preserving projections (2ddlpp) and its application to feature extraction via fuzzy set. Multimed Tools Appl 76(1):355–371

    Google Scholar 

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Acknowledgements

We are thankful to Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India, for research grant. The research work is sanctioned project titled as “Design and development of an Automatic Kinship Verification system for Indian faces with possible integration of AADHAR Database.” with Reference No. ECR/2016/001659.

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Goyal, A., Meenpal, T. Eccentricity based kinship verification from facial images in the wild. Pattern Anal Applic 24, 119–144 (2021). https://doi.org/10.1007/s10044-020-00906-4

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