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A Fusion Schema of Hand-Crafted Feature and Feature Learning for Kinship Verification

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

Abstract

The rapid progress of technology is remarkable and becomes more widespread in various forms such as social networks, smart phones, and high-definition cameras. In this context, analysing facial to kinship based on digital images is a new research topic in computer vision and has been increased dramatically in recent years. In this paper, we trying to detect the relationships between pairs of face images which is reflected a verification matter: given a pairs of face images with a view to find out and infer kin from the non-kin. For this, we proposed a method define by a fusion scheme composed of feature learning (high-level feature) and hand-crafted feature (low-level feature) along with features subtracting absolute value for face pair. For hand-crafted, we apply a histogram of oriented gradients (HOG) descriptor, while, convolutional neural net- works (CNN) is to represent the feature learning. In our experiment to validate the proposed method we apply restricted protocol setting. The proposed method is tested and evaluated on the benchmark databases KinFaceW-I and KinFaceW-II, and the verification accuracies of 68.6% and 73.5% were achieved, respectively.

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References

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

    Article  Google Scholar 

  2. Chen, X., An, L., Yang, S., Wu, W.: Kinship verification in multi-linear coherent spaces. Multimedia Tools Appl. 76(3), 4105–4122 (2015)

    Article  Google Scholar 

  3. Yan, H., Lu, J.: Facial Kinship Verification: A Machine Learning Approach. Springer, Singapore (2017)

    Google Scholar 

  4. Zhang, K., Huang, Y., Song, C., Wu, H., Wang, L.: Kinship verification with deep convolutional neural networks (2015)

    Google Scholar 

  5. Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Jie, Z.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Patt. Anal. Mach. Intell. 36(2), 331–345 (2013)

    Google Scholar 

  6. Kou, L., Zhou, X., Xu, M., Shang, Y.: Learning a genetic measure for kinship verification using facial images. Math. Probl. Eng. 2015, 5 (2015)

    Google Scholar 

  7. Lu, J., Hu, J., Zhou, X., Zhou, J., Castrilln-Santana, M., Lorenzo-Navarro, J., Kou, L., Shang, Y., Bottino, A., Vieira, T.F.: Kinship verification in the wild: the first kinship verification competition. In: IEEE International Joint Conference on Biometrics, pp. 1–6. IEEE (2014)

    Google Scholar 

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

    Google Scholar 

  9. Liu, Q., Puthenputhussery, A., Liu, C.: A novel inheritable color space with application to kinship verification. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9. IEEE (2016)

    Google Scholar 

  10. Dornaika, F., Arganda-Carreras, I., Serradilla, O.: Transfer learning and feature fusion for kinship verification. Neural Comput. Appl. 32, 7139–7151 (2019)

    Article  Google Scholar 

  11. Chergui, A., Ouchtati, S., Sequeira, J., Bekhouche, S.E., Bougourzi, F.: Kinship verification using BSIF and LBP. In: 2018 International Conference on Signal, Image, Vision and their Applications (SIVA), pp. 1–5. IEEE (2018)

    Google Scholar 

  12. Rehman, A., Khalid, Z., Asghar, M.A., Khan, M.J.: Kinship verification using deep neural network models. In: 2019 International Symposium on Recent Advances in Electrical Engineering (RAEE), vol. 4, pp. 1–6. IEEE (2019)

    Google Scholar 

  13. Chergui, A., Ouchtati, S., Mavromatis, S., Bekhouche, S.E., Sequeira, J.: Investigating deep CNNs models applied in kinship verification through facial images. In: 2019 5th International Conference on Frontiers of Signal Processing (ICFSP), pp. 82–87. IEEE (2019)

    Google Scholar 

  14. Qin, X., Liu, D., Wang, D.: A literature survey on kinship verification through facial images. Neurocomputing 377, 213–224 (2020)

    Article  Google Scholar 

  15. Wang, X., Kambhamettu, C.: Leveraging appearance and geometry for kinship verification. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5017–5021. IEEE (2014)

    Google Scholar 

  16. Boutellaa, E., Lpez, M.B., Ait-Aoudia, S., Feng, X., Hadid, A.: Kinship verification from videos using spatio-temporal texture features and deep learning (2017)

    Google Scholar 

  17. Dal Martello, M.F., Maloney, L.T.: Where are kin recognition signals in the human face? J. Vis. 6(12), 2 (2006)

    Article  Google Scholar 

  18. DeBruine, L.M., Smith, F.G., Jones, B.C., Roberts, S.C., Petrie, M., Spector, T.D.: Kin recognition signals in adult faces. Vis. Res. 49(1), 38–43 (2009)

    Article  Google Scholar 

  19. Froelich, A.G., Nettleton, D.: Does my baby really look like me? using tests for resemblance between parent and child to teach topics in categorical data analysis. J. Stat. Educ. 21(2), 1–19 (2013)

    Google Scholar 

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

    Google Scholar 

  21. Maloney, L.T., Dal Martello, M.F.: Kin recognition and the perceived facial similarity of children. J. Vis. 6(10), 4 (2006)

    Article  Google Scholar 

  22. Park, J.H., Schaller, M., Van Vugt, M.: Psychology of human kin recognition: heuristic cues, erroneous inferences, and their implications. Rev. Gen. Psychol. 12(3), 215–235 (2008)

    Article  Google Scholar 

  23. Duan, X., Tan, Z.-H.: 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 (2015)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  26. Zhou, X., Lu, J., Hu, J., Shang, Y.: Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 725–728 (2011)

    Google Scholar 

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

    Article  Google Scholar 

  28. Hu, J., Lu, J., Tan, Y., Yuan, J., Zhou, J.: Local large-margin multi-metric learning for face and kinship verification. IEEE Trans. Circ. Syst. Video Technol. 28(8), 1875–1891 (2017)

    Article  Google Scholar 

  29. Fang, Y., Yan, Y., Chen, S., Wang, H., Shu, C.: Sparse similarity metric learning for kinship verification. In: 2016 Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2016)

    Google Scholar 

  30. Liang, J., Hu, Q., Dang, C., Zuo, W.: Weighted graph embedding-based metric learning for kinship verification. IEEE Trans. Image Process. 28(3), 1149–1162 (2018)

    Article  MathSciNet  Google Scholar 

  31. Patil, H.Y., Chandra, A.: Deep learning based kinship verification on kinfacew-i dataset. In: 2019 IEEE Region 10 Conference (TENCON), TENCON 2019, pp. 2529–2532. IEEE (2019)

    Google Scholar 

  32. Chergui, A., Ouchtati, S., Sequeira, J., Bekhouche, S.E., Bougourzi, F., Telli, H.: Deep features for kinship verification from facial images. In: 2019 International Conference on Advanced Systems and Emergent Technologies (IC ASET), pp. 64–67. IEEE (2019)

    Google Scholar 

  33. Yang, Y., Wu, Q.: A novel kinship verification method based on deep transfer learning and feature nonlinear mapping. In: AIEA 2017 (2017)

    Google Scholar 

  34. Kohli, N., Vatsa, M., Singh, R., Noore, A., Majumdar, A.: Hierarchical representation learning for kinship verification. IEEE Trans. Image Process. 26(1), 289–302 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  35. Fang, R., Tang, K.D., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: 2010 IEEE International Conference on Image Processing, pp. 1577–1580. IEEE (2010)

    Google Scholar 

  36. Bottino, A., De Simone, M., Laurentini, A., Vieira, T.: A new problem in face image analysis-finding kinship clues for siblings pairs. In: ICPRAM, vol. 2, pp. 405–410 (2010)

    Google Scholar 

  37. Vieira T.F., Bottino A., Islam I.U.: Automatic verification of parent-child pairs from face images. In: Iberoamerican Congress on Pattern Recognition, pp. 326–333. Springer, Heidelberg (2013)

    Google Scholar 

  38. Xia, S., Shao, M., Fu, Y.: Toward kinship verification using visual attributes. In: Proceedings of the 21st International Conference on Pattern Recognition, ICPR2012, pp. 549–552 IEEE (2012)

    Google Scholar 

  39. Almuashi, M., Mohd Hashim, S.Z., Mohamad, D., Alkawaz, M.H., Ali, A.: Automated kinship verification and identification through human facial images: a survey. Multimed. Tools Appl. 76(1), 265–307 (2017)

    Article  Google Scholar 

  40. Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., Hospedales, T.: 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 Workshops, pp. 142–150 (2015)

    Google Scholar 

  41. Bengio, Y.: Learning Deep Architectures for AI. Now Publishers Inc. (2009)

    Google Scholar 

  42. Jung, K., Zhang, B.-T., Mitra, P.: Deep learning for the web. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1525–1526 (2015)

    Google Scholar 

  43. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675– 678 (2014)

    Google Scholar 

  44. Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey (2012)

    Google Scholar 

  45. Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Sig. Inf. Process. 3, E2 (2014)

    Article  Google Scholar 

  46. Chen, Y., Zhu, X., Gong, S.: Person re-identification by deep learning multi-scale representations. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2590–2600 (2017)

    Google Scholar 

  47. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  48. Pelin, G., Simsek, A.: Face recognition via deep stacked denoising sparse autoencoders (DSDA). Appl. Math. Comput. 355, 325–342 (2019)

    MathSciNet  MATH  Google Scholar 

  49. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  50. Wei X., Guo G., Wang H., Wan H.: A multiscale method for HOG-based face recognition. In: International Conference on Intelligent Robotics and Applications, pp. 535–545. Springer, Cham (2015)

    Google Scholar 

  51. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 1–62 (2020)

    Article  Google Scholar 

  52. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  53. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

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

    Google Scholar 

  55. Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Ensembles of deep learning models and transfer learning for ear recognition. Sensors 19(19), 4139 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  58. Nash, W., Drummond, T., Birbilis, N.: A review of deep learning in the study of materials degradation. npj Mater. Degrad. 2(1), 1–12 (2018)

    Article  Google Scholar 

  59. Duan, X., Tan, Z.-H.: Neighbors based discriminative feature difference learning for kinship verification. In: International Symposium on Visual Computing, pp. 258–267. Springer, Cham (2015)

    Google Scholar 

  60. Dong, J., Ao, X., Su, S., Li, S.: Kinship classification based on discriminative facial patches. In: 2014 IEEE Visual Communications and Image Processing Conference, pp. 157–160. IEEE (2014)

    Google Scholar 

  61. Kobayashi, T., Hidaka, A., Kurita, T.: Selection of histograms of oriented gradients features for pedestrian detection. In: International Conference on Neural Information Processing, pp. 598–607. Springer, Heidelberg (2008)

    Google Scholar 

  62. Chollet, F.: Keras: deep learning library for theano and tensorflow, vol. 7, no. 8, p. T1 (2015)

    Google Scholar 

  63. Wang, M., Li, Z., Xiangbo Shu, J., Tang, J.: Deep kinship verification. In 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  65. Yan, H.: Kinship verification using neighborhood repulsed correlation metric learning. Image Vis. Comput. 60, 91–97 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Mohammed Ali Almuashi

  2. Jeddah University, Jeddah, Kingdom of Saudi Arabia

    Mohammed Ali Almuashi

  3. Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia

    Siti Zaiton Mohd Hashim, Nooraini Yusoff & Khairul Nizar Syazwan

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  1. Mohammed Ali Almuashi
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  2. Siti Zaiton Mohd Hashim
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  3. Nooraini Yusoff
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  4. Khairul Nizar Syazwan
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Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

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Almuashi, M.A., Hashim, S.Z.M., Yusoff, N., Syazwan, K.N. (2021). A Fusion Schema of Hand-Crafted Feature and Feature Learning for Kinship Verification. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_94

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