Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah

Parallel Spatial Pyramid Convolutional Neural Network for Kinship Verification from Face Images

Reza Fuad Rachmadi* -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
I Ketut Eddy Purnama -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
In this paper, we proposed a parallel spatial pyramid CNN classifier for image-based kinship verification problem. Two face images that compared for kinship verification treated as input for each parallel convolutional network of our classifier. Each parallel convolutional network constructed using spatial pyramid CNN classifier. At the end of the convolutional network, we use three fully connected layers to combine each spatial pyramid CNN features and decided the final kinship prediction. We tested the proposed classifier using large-scale kinship verification dataset, called FIW dataset, consists of seven kinship problems from 1,000 families. In our approach, we treated each kinship problem as a binary classification problem with two output. We train our classifier separately for each kinship problem with same training configuration. Overall, our proposed method can achieve an average accuracy of more than 60% and outperform the baseline method.
Keywords
image-based kinship verification; parallel spatial pyramid CNN; deep spatial pyramid features
Full Text:

How to cite:

Article Metrics:

Article Info
Submitted: 2018-08-06
Published: 2018-10-31
Section: Articles
Language: ID
Statistics: 70 32
In Volume 6, Issue 4, Year 2018 (October 2018)
  1. J. P. Robinson, M. Shao, Y. Wu, and Y. Fu, “Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks,” In Proceedings
  2. of the 2016 ACM on Multimedia Conference, 2016. pp. 242–246.
  3. S. Wang, J. P. Robinson, and Y. Fu, “Kinship Verification on Families in the Wild with Marginalized Denoising Metric Learning,” In 12th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2017. pp. 216–221.
  4. S. Xia, M. Shao, and Y. Fu, “Kinship verification through transfer learning,” In International Joint Conference on Artificial Intelligent (IJCAI), 2011. pp. 2539–2544.
  5. S. Xia, M. Shao, J. Luo, and Y. Fu, “Understanding Kin Relationships in a
  6. Photo,” IEEE Transactions on Multimedia, Vol. 14, No. 4, pp. 1046–1056, 2012.
  7. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer
  8. vision and pattern recognition (CVPR), 2016. pp. 770–778.
  9. A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” In Advances in Neural
  10. Information Processing Systems (NIPS), 2012. pp. 1097–1105.
  11. R. F. Rachmadi, Y. Komokata, K. Uchimura, and G. Koutaki, “Road sign classification system using cascade convolutional neural network,“ International Journal of Innovative Computing, Information, and Control (IJICIC), Vol. 13, No.1, pp. 95–109, 2017.
  12. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” In European conference on computer vision (ECCV), 2016. pp. 21–37.
  13. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. pp. 779–788.
  14. J. Redmon, and A. Farhadi, "YOLO9000: Better, Faster, Stronger," In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517–6525.
  15. Y. Qian and P. C. Woodland, “Very deep convolutional neural networks for robust speech recognition,” In Spoken Language Technology Workshop (SLT), 2016. pp. 481–488.
  16. Y. Zhang, M. Pezeshki, P. Brakel, S. Zhang, C. Laurent, Y. Bengio, and A. Courville, “Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks,” In Proceedings of Interspeech, 2016. pp. 410-414.
  17. R. F. Rachmadi, K. Uchimura, and G. Koutaki, “Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image. In 11th International Student Conference on Advanced Science and Technology (ICAST), 2016.
  18. O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep Face Recognition,” In Proceedings of the British Machine Vision Conference (BMVC), 2015.
  19. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional Architecture for Fast
  20. Feature Embedding,” In Proceedings of the 22nd ACM International Conference on Multimedia (MM ’14), 2014. pp. 675–678.
  21. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), Vol. 115, No. 3, pp. 211–252, 2015.
  22. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014.

Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer License URL: https://creativecommons.org/licenses/by-sa/4.0

The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro as publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. 

JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTSiskom journal are the sole and exclusive responsibility of their respective authors and advertisers.

The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTSiskom]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :  

Eko Didik Widianto (Editor-in-Chief)
Editorial Office of Jurnal Teknologi dan Sistem Komputer
Department of Computer Engineering, Universitas Diponegoro
Jl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275
Telp./Fax: +62-24-76480609
Email: jtsiskom@ce.undip.ac.id