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JAIT 2023 Vol.14(2): 160-167
doi: 10.12720/jait.14.2.160-167

Face Detection in Close-up Shot Video Events Using Video Mining

Amjad Rehman Khan 1, Majid Harouni 2,*, Sepideh Sharifi 2, Saeed Ali Bahaj 3, and Tanzila Saba 1
1. Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University Riyadh 11586, Saudi Arabia; Email: {arkhan, tsaba}@psu.edu.sa (A.R.K., T.S.)
2. Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran; Email: sepideh.sh71@yahoo.com (S.S.)
3. MIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia; Email: s.bahaj@psau.edu.sa (S.A.B.)
*Correspondence: majid.harouni@gmail.com (M.H.)

Manuscript received August 10, 2022; revised September 12, 2022; accepted September 28, 2022; published March 8, 2023.

Abstract—Face detection and recognition in abrupt dynamic images is still challenging due to high complexity of images. To tackle this issue, we employed Gray-Level Co-occurrence Matrix (GLCM) to convert large video into smaller consequential sections containing sequence information from a series of images. GLCM is a matrix associated with the relationship between the values of adjacent pixels in an image. The proposed method is composed of two stages. First, the video is taken as input using the histogram difference method. Features are extracted using co-occurrence matrix of images, statistical methods, and the border of sudden shots extracted from the video. Second, face recognition with the Viola-Jones algorithm is performed on the sudden shots extracted in the first step. Thus, the face is extracted by video data mining in output in close shots. In this method, we compared the parameter model in three windows (3, 5 and 7) and threshold limit for detecting abrupt cuts between values (0.1, 0.5, 1.5, 1.5 and 2) for each window. The highest percentage of face detection is attained by considering the maximum percentage of abrupt cuts in the 5×5 window with a threshold value of 1.

Keywords—close shot, statistical methods, co-occurrence matrix of images, face detection, security, technological development

Cite: Amjad Rehman Khan, Majid Harouni, Sepideh Sharifi, Saeed Ali Bahaj, and Tanzila Saba, "Face Detection in Close-up Shot Video Events Using Video Mining," Journal of Advances in Information Technology, Vol. 14, No. 2, pp. 160-167, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.