A New Algorithm to Detect Occluded Face from a Head Viewpoint using Hough Transform and Skin Ratio

Authors

  • Theekapun CHAROENPONG Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhonnayok 26120
  • Patarida SANITTHAI Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhonnayok 26120

Keywords:

Surveillance system, multiple viewpoint occluded face detection, helmet detection, mask detection

Abstract

The performance of current algorithms used in occluded face detection for surveillance systems is limited when detecting a face covered with an obstacle, or a non-frontal view of the face. Therefore, a method able to capture a face from any viewpoint is necessary. In this paper, we propose a new algorithm by using 2 subdivision regions and skin ratio for detecting occluded faces from any head viewpoint during +90 degrees to -90 degrees around the yaw axis. This algorithm consists of 3 steps: head region identification, skin extraction, and occluded face detection. First, the system is fed with an image sequence capturing the whole target body, to define the head region. The head region is detected using a blob technique under an experimental condition. Second, skin data is extracted, for computing skin ratio. Skin color is considered in multiple color spaces, and compared with a database by Mahalanobis Distance technique. Third, for occluded face detection, the human head area is equally divided into 2 vertical regions. The skin ratio of each part is used as a criterion for occlusion detection. To test the performance of the proposed algorithm, data from 35 subjects is used. The data of a subject is captured from any viewpoint of the head, varying from +90 degrees to -90 degrees. As this paper aims to develop surveillance systems, obstacles covering the whole face are focused on, such as helmets and masks. The accuracy rate of non-occluded face and occluded face detection is 98.81 and 94.90 %, respectively. The average accuracy rate is 95.39 %. The advantage of this method over recent research is that this is the first method to detect an occluded face from any viewpoint of the head varying from +90 degrees to -90 degrees.

doi:10.14456/WJST.2015.4

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References

AM Martinez and R Benavente. The AR Face Database. CVC Technical Report 24, 1998.

R Min, A D'angelo and JL Dugelay. Efficient scarf detection prior to face recognition. In: Proceedings of the 18th European Signal Processing Conference, Aalborg, Denmark, 2010, p. 259-63.

GN Priya and RSDW Banu. Detection of occluded face image using mean based weight matrix and support vector machine. J. Comput. Sci. 2012; 8, 1184-90.

SM Yoon and SC Kee. Detection of partially occluded face using support vector machines. In: Proceedings of the IAPR Workshop on Machine Vision Applications, Nara, Japan, 2002, p. 546-9.

CY Wen, SH Chiu, J Liaw and C Lu. The safety helmet detection for ATM’s surveillance system via the modified Hough transform. In: Proceedings of the IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, Taipei, Taiwan, 2003, p. 364-9.

DT Lin and MJ Liu. Face occlusion detection for automated teller machine surveillance. Adv. Image Video Tech. 2006; 4319, 641-51.

G Kim, JK Suhr, HG Jung and J Kim. Face occlusion detection by using B-spline active contour and skin color information. In: Proceedings of the 11th International Conference on Control Automation Robotics & Vision, 2010, p. 627-32.

J Chiverton. Helmet presence classification with motorcycle detection and tracking. IET Intell. Transport Syst. 2012; 6, 259-69.

T Charoenpong. Face occlusion detection by Ellipse fitting and skin color ratio. In: Proceedings of the Burapha University International Conference, Chonburi, Thailand, 2013, p. 1145-51.

T Charoenpong, C Nuthong and U Watchareeruetai. A new method for occluded face detection from single viewpoint of head. In: Proceedings of the 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Nakhon Ratchasima, Thailand, 2014, p. 1-5.

S Thakur, S Paul, A Mondal and SW Das. Face detection using skin tone segmentation. In: Proceedings of the 2011 World Congress on Information and Communication Technologies, Mumbai, 2011, p. 53-60.

P Ng and CM Pun. Skin color segmentation by texture feature extraction and K-mean clustering. In: Proceedings of the 3rd International Conference on Computational Intelligence, Communication Systems and Networks, Bali, 2011, p. 213-8.

MA Zia, U Ansari, M Jamil, O Gillani and Y Ayas. Face and eye detection in images using skin color segmentation and circular Hough transform. In: Proceedings of the International Conference on Robotics and Emerging Allied Technologies in Engineering, Islamabad, 2014, p. 211-3.

MM Hasan and MF Hossain. Facial features detection in color images based on skin color segmentation. In: Proceedings of the International Conference on Informatics, Electronics & Vision, Dhaka, 2014, p. 1-5.

S Singh, DS Chauhan, M Vatsa and R Singh. A robust skin color based face detection algorithm. Tamkang J. Sci. Eng. 2003; 6, 227-34.

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Published

2014-10-26

How to Cite

CHAROENPONG, T., & SANITTHAI, P. (2014). A New Algorithm to Detect Occluded Face from a Head Viewpoint using Hough Transform and Skin Ratio. Walailak Journal of Science and Technology (WJST), 12(1), 35–49. Retrieved from https://wjst.wu.ac.th/index.php/wjst/article/view/1204

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Section

Research Article