Pengenalan sketsa wajah menggunakan principle component analysis sebagai aplikasi forensik

Face sketch recognition using principal component analysis for forensics application

*Endina Putri Purwandari orcid scopus  -  Department of Informatics, Universitas Bengkulu, Indonesia
Aan Erlansari orcid scopus  -  Department of Informatics, Universitas Bengkulu, Indonesia
Andang Wijanarko  -  Department of Informatics, Universitas Bengkulu, Indonesia
Erich Adinal Adrian  -  Department of Informatics, Universitas Bengkulu, Indonesia
Received: 17 Jul 2019; Revised: 20 Apr 2020; Accepted: 24 Apr 2020; Published: 31 Jul 2020; Available online: 5 May 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Section: Original Research Articles
Language: ID
Statistics: 317 72
Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.
Keywords: sketch; face; principal component analysis; digital image; forensics

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  1. X. Tang and X. Wang, “Face sketch synthesis and recognition,” in 9th IEEE International Conference on Computer Vision, Nice, France, Oct. 2003, pp.687-694. doi: 10.1109/ICCV.2003.1238414
  2. S. Nagpal, V. Mayank, and R. Singh, "Sketch recognition: what lies ahead?," Image and Vision Computing, vol. 55, no. 1, pp. 9-13, 2016. doi: 10.1016/j.imavis.2016.03.019
  3. E. P. Purwandari, Konsep dan Teori Pengolahan Citra Digital. Universitas Bengkulu, Bengkulu: UNIB Press, 2018.
  4. R. F. Rachmadi and I. K. E. Purnama, “Parallel spatial pyramid convolutional neural network untuk verifikasi kekerabatan berbasis citra wajah,” Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 4, pp. 152-157, 2018. doi: 10.14710/jtsiskom.6.4.2018.152-157
  5. A. R. Syakhala, D. Puspitaningrum, and E.P. Purwandari, “Perbandingan metode principal component analysis (PCA) dengan metode hidden markov model (HMM) dalam pengenalan identitas seseorang melalui wajah,” Rekursif: Jurnal Informatika, vol. 3, no. 2, pp. 68-81, 2015.
  6. F. Deng-Ping et al., "Face sketch synthesis style similarity: a new structure co-occurrence texture measure," arXiv:1804.02975, 2018.
  7. S. Pratama and J. Adler, “Pengenalan wajah untuk pencarian data buronan melalui gambar sketsa,” Skripsi, Universitas Komputer Indonesia, Bandung, Indonesia, 2016.
  8. D. E. Pratiwi and Harjoko, A., “Implementasi pengenalan wajah menggunakan PCA (principal component analysis),” IJEIS (Indonesian Journal of Electronics and Instrumentations System), vol. 3, no. 2, pp. 175-184, 2013.
  9. R. V. Priya and S. V. Santhi, “PCA based face sketch synthesis using Eigen transformation,” International Journal of Computer Science and Mobile Computing, vol. 5, no. 3, pp. 346-350, 2016.
  10. U. T. Tayade, S. Biday, and L. Ragha, “Forensic sketch-photo matching using LFDA,” International Journal of Soft Computing and Engineering, vol. 3, no. 4, pp. 242-246, 2013.
  11. S. E. Lahlali, A. Sadiq, and S. Mbarki, "Face sketch recognition system: a content based image retrieval approach," in 4th IEEE International Colloquium on Information Science and Technology (CIST), Tangier, Morocco, Oct. 2016, pp. 428-433. doi: 10.1109/CIST.2016.7805085
  12. Chinese University of Hong Kong, “CUHK face sketch database (CUFS),” 2011. [Online]. Available:
  13. R. Vidal, Y. Ma, and S. Sastry, “Generalized principal component analysis (GPCA),” IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 12, pp. 1945-1959, 2005. doi: 10.1109/TPAMI.2005.244
  14. K. I. Kim, K. Jung, and H. J. Kim, "Face recognition using kernel principle component analysis," IEEE Signal Processing Letter, vol. 9, no. 2, pp. 40-42, 2002. doi: 10.1109/97.991133
  15. A. Selim and R. M. Haralick, "Probabilistic vs. geometric similarity measures for image retrieval," in IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, USA, Jun. 2000, pp. 357-362, doi: 10.1109/CVPR.2000.854847
  16. A. Barbadekar and P. Kulkarni, “A survey of face recognition from sketches,” International Journal of Latest Trends in Engineering and Technology (IJLTET), vol. 6, no. 3, pp. 150-158, 2016.
  17. A. Tharwar, H. Mahdi, A. El-Hennawy, and A. E. Hassanien, “Face sketch recognition using local invariant features,” in th International Conference of Soft Computing and Pattern Recognition, Fukuoka, Japan, Nov. 2015, pp. 117-122. doi: 10.1109/SOCPAR.2015.7492793

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