Klasifikasi Kelompok Usia Melalui Citra Wajah Berbasis Image Texture Analysis pada Sistem Automatic Video Filtering

  • Sudirman S Panna Universitas Ichsan Gorontalo
  • Betrisandi Universitas Ichsan Gorontalo
Keywords: face recognition, Age Classification, LBP, GLCM

Abstract

Nowadays information technology makes it easier for everyone to access various information, this easiness harms minors, because it is possible to access adult content from the internet, television or mobile devices. The problem is the unavailability of the system for filtering and authentication to get information by the face. The face contains information related to personal characteristics such as age, etc. feature extraction is an important stage in the face recognition process. This study proposed local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) as feature extraction to describe face feature, and we use artificial neural network to classify the human age, the experiment result after calculation with confusion matrix obtained average acceleration of 94.8%, precision of 93.7% and recall of 92.3%, it’s performance measure obtained proposed method can be described face feature it well, so that, the proposed method can be used as reference material to development video filtering system by age of the users in access information based on video especially pornography and violence content.

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References

P.-K. Sai, J.-G. Wang, and E.-K. Teoh, “Facial age range estimation with extreme learning machines,” Neurocomputing, vol. 149, pp. 364–372, 2015.

S. E. Bekhouche, A. Ouafi, A. Benlamoudi, A. Taleb-Ahmed, and A. Hadid, “Facial age estimation and gender classification using multi level local phase quantization,” 3rd Int. Conf. Control. Eng. Inf. Technol. CEIT 2015, no. September, pp. 1–4, 2015.

A. Günay and V. V Nabiyev, “Age estimation based on hybrid features of facial images,” in Information Sciences and Systems 2015, Springer, 2016, pp. 295–304.

Y. Zheng, H. Yao, Y. Zhang, and P. Xu, “Age classification based on back-propagation network,” in Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service, 2013, pp. 319–322.

S. E. Bekhouche, A. Ouafi, A. Taleb-Ahmed, A. Hadid, and A. Benlamoudi, “Facial age estimation using BSIF and LBP,” in International Conference on Electrical Engineering (ICEEB’14), 2014, pp. 1–5.

J. Liu, Y. Ma, L. Duan, F. Wang, and Y. Liu, “Hybrid constraint SVR for facial age estimation,” Signal Processing, vol. 94, pp. 576–582, Jan. 2014.

N. K. El Abbadi and A. A. A. Qazzaz, “Detection and segmentation of human face,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 4, no. 2, pp. 90–94, 2015.

R. Jana, D. Datta, and R. Saha, “Age estimation from face image using wrinkle features,” in Procedia Computer Science, 2015, vol. 46, no. Icict 2014, pp. 1754–1761.

M. Pratiwi, J. Harefa, and S. Nanda, “Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network,” Procedia Comput. Sci., vol. 59, pp. 83–91, 2015.

Y. Fu, “FG-NET Aging Database,” 2014. [Online]. Available: https://yanweifu.github.io/FG_NET_data/index.html.

V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1867–1874.

D. Tiwari and V. Tyagi, “A novel scheme based on local binary pattern for dynamic texture recognition,” Comput. Vis. Image Underst., vol. 150, pp. 58–65, 2016.

F. Yuan, J. Shi, X. Xia, Y. Yang, Y. Fang, and R. Wang, “Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification.,” KSII Trans. Internet Inf. Syst., vol. 10, no. 4, 2016.

Y. Kaya, Ö. F. Ertuğrul, and R. Tekin, “Two novel local binary pattern descriptors for texture analysis,” Appl. Soft Comput., vol. 34, pp. 728–735, 2015.

S. Yahia, Y. Ben Salem, and M. N. Abdelkrim, “3D face recognition using local binary pattern and grey level co-occurrence matrix,” in 2016 17th international conference on sciences and techniques of automatic control and computer engineering (STA), 2016, pp. 328–338.

X. Zhang, J. Cui, W. Wang, and C. Lin, “A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm,” Sensors, vol. 17, no. 7, p. 1474, 2017.

B. Pathak and D. Baroaoah, “Texture Analysis Based on The gray-Level Co-Occurrence matrix Considering Possible orientations,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 2, no. 9, pp. 4206–4212, 2013.

M. M. Kasar, D. Bhattacharyya, and T. Kim, “Face recognition using neural network: a review,” Int. J. Secur. Its Appl., vol. 10, no. 3, pp. 81–100, 2016.

N. T. Deshpande and S. Ravishankar, “Face Detection and Recognition using Viola-Jones algorithm and Fusion of PCA and ANN,” Adv. Comput. Sci. Technol., vol. 10, no. 5, pp. 1173–1189, 2017.

Published
2019-12-09
How to Cite
Panna, S. S., & Betrisandi. (2019). Klasifikasi Kelompok Usia Melalui Citra Wajah Berbasis Image Texture Analysis pada Sistem Automatic Video Filtering. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 429 - 434. https://doi.org/10.29207/resti.v3i3.1280
Section
Information Technology Articles