A Review of Content-Based Video Retrieval Techniques for Person Identification

Authors

  • Syahmi Syahiran Ahmad Ridzuan School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • Zaid Omar Department of Electric and Computer Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • Usman Ullah Sheikh Department of Electric and Computer Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

DOI:

https://doi.org/10.11113/elektrika.v18n3.196

Keywords:

Clothing Recognition, Content-Based Video Retrieval, Ethnic Recognition, Gender Recognition, Person Identification.

Abstract

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification.

References

Bertillon, A., 1896. The Bertillon system of identification. McClaughry, Ed., Chicago, IL.

Julia Alexander, 2018. What is YouTube demonetization? An ongoing, comprehensive history - Polygon. Available at: https://www.polygon.com/2018/5/10/17268102/youtube-demonetization-pewdiepie-logan-paul-casey-neistat-philip-defranco. [Accessed 18 September 2018].

Zhang, H. J., Wu, J., Zhong, D. and Smoliar, S. W., 1997. An integrated system for content-based video retrieval and browsing. Pattern recognition, 30(4), pp.643-658.

Hu, W., Xie, N., Li, L., Zeng, X. and Maybank, S., 2011. A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6), pp.797-819.

Juan, K. and Cuiying, H., 2010, July. Content-based video retrieval system research. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 4, pp. 701-704). IEEE.

Chicago Police Department, 2013. How to describe a suspect. Available at: https://portal. chicagopolice.org/portal/page/portaI/ClearPath/Get%20InvoIved/Hotlines%20and%20CPD%20Contacts/How%20to%20Describe %20a%20Suspect

Goldstein, A.J., Harmon, L.D. and Lesk, A.B., 1971. Identification of human faces. Proceedings of the IEEE, 59(5), pp.748-760.

Kanade, T., 1977. Computer recognition of human faces

Paul, M., Haque, S. M. and Chakraborty, S., 2013. Human detection in surveillance videos and its applications-a review. EURASIP Journal on Advances in Signal Processing, 2013(1), p.176.

Nguyen, D. T., Li, W. and Ogunbona, P.O., 2016. Human detection from images and videos: a survey. Pattern Recognition, 51, pp.148-175.

Heckathorn, D. D., Broadhead, R. S. and Sergeyev, B., 2001. A methodology for reducing respondent duplication and impersonation in samples of hidden populations. Journal of Drug Issues, 31(2), pp.543-564.

Gutta, S., Huang, J. R., Jonathon, P. and Wechsler, H., 2000. Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Transactions on neural networks, 11(4), pp.948-960.

González-Briones, A., Villarrubia, G., De Paz, J.F. and Corchado, J.M., 2018. A multi-agent system for the classification of gender and age from images. Computer Vision and Image Understanding.

Azzopardi, G., Greco, A., Saggese, A. and Vento, M., 2018. Fusion of domain-specific and trainable features for gender recognition from face images. IEEE Access, 6, pp.24171-24183.

Rodríguez, P., Cucurull, G., Gonfaus, J. M., Roca, F. X. and Gonzalez, J., 2017. Age and gender recognition in the wild with deep attention. Pattern Recognition, 72, pp.563-571.

Raza, M., Sharif, M., Yasmin, M., Khan, M. A., Saba, T. and Fernandes, S. L., 2018. Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Future Generation Computer Systems, 88, pp.28-39.

Tariq, U., Hu, Y. and Huang, T. S., 2009, November. Gender and ethnicity identification from silhouetted face profiles. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 2441-2444). IEEE.

Mozaffari, S., Behravan, H. and Akbari, R., 2010, August. Gender classification using single frontal image per person: combination of appearance and geometric based features. In Pattern Recognition (ICPR), 2010 20th International Conference on (pp. 1192-1195). IEEE.

Hatipoglu, B. and Kose, C., 2017, October. A gender recognition system from facial images using SURF based BoW method. In Computer Science and Engineering (UBMK), 2017 International Conference on (pp. 989-993). IEEE.

Orozco, C. I., Iglesias, F., Buemi, M.E. and Berlles, J. J., 2017. Real-time gender recognition from face images using deep convolutional neural network.

Cirne, M. V. M. and Pedrini, H., 2017, October. Gender recognition from face images using a geometric descriptor. In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on (pp. 2006-2011). IEEE.

Nistor, S. C., Marina, A. C., Darabant, A. S. and Borza, D., 2017, September. Automatic gender recognition for “in the wild†facial images using convolutional neural networks. In Intelligent Computer Communication and Processing (ICCP), 2017 13th IEEE International Conference on (pp. 287-291). IEEE.

Moghaddam, B. and Yang, M.H., 2002. Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), pp.707-711.

Liu, H. H., Xu, S. S. D., Chiu, C. C. and Chiu, S. Y., 2017, June. Gender recognition technology of whole-body image. In Consumer Electronics-Taiwan (ICCE-TW), 2017 IEEE International Conference on (pp. 263-264). IEEE.

Weng, T., Yuan, Y., Shen, L. and Zhao, Y., 2013, October. Clothing image retrieval using color moment. In Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on (pp. 1016-1020). IEEE.

Hidayati, S. C., You, C. W., Cheng, W. H. and Hua, K. L., 2018. Learning and recognition of clothing genres from full-body images. IEEE transactions on cybernetics, 48(5), pp.1647-1659.

Chao, X., Huiskes, M. J., Gritti, T. and Ciuhu, C., 2009, October. A framework for robust feature selection for real-time fashion style recommendation. In Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics (pp. 35-42). ACM.

Yang, M. and Yu, K., 2011, September. Real-time clothing recognition in surveillance videos. In Image Processing (ICIP), 2011 18th IEEE International Conference on (pp. 2937-2940). IEEE.

Kurnianggoro, L. and Jo, K. H., 2017, October. Identification of pedestrian attributes using deep network. In Industrial Electronics Society, IECON 2017-43rd Annual Conference of the IEEE (pp. 8503-8507). IEEE.

Sun, L., Aragon-Camarasa, G., Rogers, S., Stolkin, R. and Siebert, J. P., 2017, September. Single-shot clothing category recognition in free-configurations with application to autonomous clothes sorting. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on (pp. 6699-6706). IEEE.

Huang, C. Q., Chen, J. K., Pan, Y., Lai, H. J., Yin, J. and Huang, Q. H., 2018. Clothing landmark detection using deep networks with prior of key point associations. IEEE transactions on cybernetics, (99), pp.1-11.

Guo, G. and Mu, G., 2010, June. A study of large-scale ethnicity estimation with gender and age variations. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on (pp. 79-86). IEEE.

Lin, H., Lu, H. and Zhang, L., 2006. A new automatic recognition system of gender, age and ethnicity. In Proceedings of WCICA (Vol. 2, pp. 9988-9991).

Mohammad, A. S. and Al-Ani, J. A., 2017, September. Towards ethnicity detection using learning-based classifiers. In Computer Science and Electronic Engineering (CEEC), 2017 (pp. 219-224). IEEE.

Chrispy, 2004. Ethnic skintones. Available at: http://www.coolminiornot.com/articles/1310-ethnic-skintones. [Accessed 18 September 2018].

Xiao, K., Yates, J. M., Zardawi, F., Sueeprasan, S., Liao, N., Gill, L., Li, C. and Wuerger, S., 2017. Characterizing the variations in ethnic skin colours: a new calibrated data base for human skin. Skin Research and Technology, 23(1), pp.21-29.

Torres, V., Herane, M. I., Costa, A., Martin, J. P. and Troielli, P., 2017. Refining the ideas of "ethnic" skin. Anais brasileiros de dermatologia, 92(2), pp.221-225.

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Published

2019-12-19

How to Cite

Ahmad Ridzuan, S. S., Omar, Z., & Sheikh, U. U. (2019). A Review of Content-Based Video Retrieval Techniques for Person Identification. ELEKTRIKA- Journal of Electrical Engineering, 18(3), 49–56. https://doi.org/10.11113/elektrika.v18n3.196

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