INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.11, Nov. 2018

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Full Text (PDF, 1033KB), PP.28-37


Views:11   Downloads:0

Author(s)

D. M. Anisuzzaman, A. F. M. Saifuddin Saif

Index Terms

Iris detection;frequent iris movement detection;suspicious activity detection; activity recognition;questionable observer detection

Abstract

This research presents a framework to detect a questionable observer depending on a specific activity named “frequent iris movement”. We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation we have detected the midpoint between left and right irises; and based on some characteristics of midpoint movement we have detected a specific activity – frequent iris movement. Depending on this activity we are declaring someone as questionable observer. To validate this research we have created our own dataset with 86 videos, where 15 individuals have volunteered. We have achieved an accuracy of 90% for the first 100 frames or 3.33 seconds of each of our videos and an accuracy of 93% for the first 150 frames or 5.00 seconds of each of our videos. No work has been done yet on basis of this specific activity to detect someone as questionable and furthermore our work outperforms most of the existing work on questionable observe detection and suspicious activity recognition. 

Cite This Paper

D. M. Anisuzzaman, A. F. M. Saifuddin Saif, "Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.11, pp. 28-37, 2018.DOI: 10.5815/ijigsp.2018.11.04

Reference

[1]Takai, Miwa. "Detection of suspicious activity and estimate of risk from human behavior shot by surveillance camera." In Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, pp. 298-304. IEEE, 2010.

[2]Doewes, Afrizal, Sri Edi Swasono, and Bambang Harjito. "Feature selection on Human Activity Recognition dataset using Minimum Redundancy Maximum Relevance." In Consumer Electronics-Taiwan (ICCE-TW), 2017 IEEE International Conference on, pp. 171-172. IEEE, 2017.

[3]Dhulekar, P. A., S. T. Gandhe, Anjali Shewale, Sayali Sonawane, and Varsha Yelmame. "Motion estimation for human activity surveillance." In Emerging Trends & Innovation in ICT (ICEI), 2017 International Conference on, pp. 82-85. IEEE, 2017.

[4]Hsu, Yu-Liang, Shyan-Lung Lin, Po-Huan Chou, Hung-Che Lai, Hsing-Cheng Chang, and Shih-Chin Yang. "Application of nonparametric weighted feature extraction for an inertial-signal-based human activity recognition system." In Applied System Innovation (ICASI), 2017 International Conference on, pp. 1718-1720. IEEE, 2017.

[5]Xu, Wanru, Zhenjiang Miao, Xiao-Ping Zhang, and Yi Tian. "Learning a hierarchical spatio-temporal model for human activity recognition." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 1607-1611. IEEE, 2017.

[6]Shakya, Subarna, Suman Sharma, and Abinash Basnet. "Human behavior prediction using facial expression analysis." In Computing, Communication and Automation (ICCCA), 2016 International Conference on, pp. 399-404. IEEE, 2016.

[7]Hassner, Tal, Yossi Itcher, and Orit Kliper-Gross. "Violent flows: Real-time detection of violent crowd behavior." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pp. 1-6. IEEE, 2012.

[8]Karagiannaki, Katerina, Athanasia Panousopoulou, and Panagiotis Tsakalides. "An online feature selection architecture for Human Activity Recognition." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 2522-2526. IEEE, 2017.

[9]Mehran, Ramin, Alexis Oyama, and Mubarak Shah. "Abnormal crowd behavior detection using social force model." In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 935-942. IEEE, 2009.

[10]Gowda, Shreyank N. "Human activity recognition using combinatorial Deep Belief Networks." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on, pp. 1589-1594. IEEE, 2017.

[11]Boufama, Boubakeur, Pejman Habashi, and Imran Shafiq Ahmad. "Trajectory-based human activity recognition from videos." In Advanced Technologies for Signal and Image Processing (ATSIP), 2017 International Conference on, pp. 1-5. IEEE, 2017.

[12]Uddin, Md Zia, Weria Khaksar, and Jim Torresen. "Human activity recognition using robust spatiotemporal features and convolutional neural network." In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference on, pp. 144-149. IEEE, 2017.

[13]Yasin, Hashim, and Shoab Ahmad Khan. "Moment invariants based human mistrustful and suspicious motion detection, recognition and classification." In Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International Conference on, pp. 734-739. IEEE, 2008.

[14]Matsui, Shinya, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, and Koichi Shinoda. "User adaptation of convolutional neural network for human activity recognition." In Signal Processing Conference (EUSIPCO), 2017 25th European, pp. 753-757. IEEE, 2017.

[15]Chen, Zhenghua, Le Zhang, Zhiguang Cao, and Jing Guo. "Distilling the Knowledge from Handcrafted Features for Human Activity Recognition." IEEE Transactions on Industrial Informatics (2018).

[16]Sunkad, Zubin A. "Feature Selection and Hyperparameter Optimization of SVM for Human Activity Recognition." In Soft Computing & Machine Intelligence (ISCMI), 2016 3rd International Conference on, pp. 104-109. IEEE, 2016.

[17]Barr, Jeremiah R., Kevin W. Bowyer, and Patrick J. Flynn. "Detecting questionable observers using face track clustering." In Applications of Computer Vision (WACV), 2011 IEEE Workshop on, pp. 182-189. IEEE, 2011.

[18]Zhao, Kun, Wei Xi, Zhiping Jiang, Zhi Wang, Hongliang Luo, Jizhong Zhao, and Xiaobin Zhang. "Leveraging Topic Model for CSI Based Human Activity Recognition." In Mobile Ad-Hoc and Sensor Networks (MSN), 2016 12th International Conference on, pp. 23-30. IEEE, 2016.

[19]Maglogiannis, Ilias, Demosthenes Vouyioukas, and Chris Aggelopoulos. "Face detection and recognition of natural human emotion using Markov random fields." Personal and Ubiquitous Computing 13, no. 1 (2009): 95-101.

[20]Cheng, Long, Yani Guan, Kecheng Zhu, Yiyang Li, and Ruokun Xu. "Accelerated Sparse Representation for Human Activity Recognition." In Information Reuse and Integration (IRI), 2017 IEEE International Conference on, pp. 245-252. IEEE, 2017.

[21]De Silva, Liyanage C., Tsutomu Miyasato, and Ryohei Nakatsu. "Facial emotion recognition using multi-modal information." In Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on, vol. 1, pp. 397-401. IEEE, 1997.

[22]Li, Kang, Xiaoguang Zhao, Jiang Bian, and Min Tan. "Sequential learning for multimodal 3D human activity recognition with Long-Short Term Memory." In Mechatronics and Automation (ICMA), 2017 IEEE International Conference on, pp. 1556-1561. IEEE, 2017.

[23]Lee, Song-Mi, Heeryon Cho, and Sang Min Yoon. "Statistical noise reduction for robust human activity recognition." In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference on, pp. 284-288. IEEE, 2017.

[24]Aramvith, Supavadee, Suree Pumrin, Thanarat Chalidabhongse, and Supakorn Siddhichai. "Video processing and analysis for surveillance applications." In Intelligent Signal Processing and Communication Systems, 2009. ISPACS 2009. International Symposium on, pp. 607-610. IEEE, 2009.

[25]Li, Wanqing, Igor Kharitonenko, Serge Lichman, and Chaminda Weerasinghe. "A prototype of autonomous intelligent surveillance cameras." In Video and Signal Based Surveillance, 2006. AVSS'06. IEEE International Conference on, pp. 101-101. IEEE, 2006.

[26]Chen, Wen-Hui, Carlos Andrés Betancourt Baca, and Chih-Hao Tou. "LSTM-RNNs combined with scene information for human activity recognition." In e-Health Networking, Applications and Services (Healthcom), 2017 IEEE 19th International Conference on, pp. 1-6. 2017.

[27]Savvaki, Sofia, Grigorios Tsagkatakis, Athanasia Panousopoulou, and Panagiotis Tsakalides. "Matrix and Tensor Completion on a Human Activity Recognition Framework." IEEE journal of biomedical and health informatics 21, no. 6 (2017): 1554-1561.

[28]Jarraya, Amina, Khedija Arour, Amel Bouzeghoub, and Amel Borgi. "Feature selection based on Choquet integral for human activity recognition." In Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on, pp. 1-6. IEEE, 2017.

[29]Elhamod, Mohannad, and Martin D. Levine. "Automated real-time detection of potentially suspicious behavior in public transport areas." IEEE Transactions on Intelligent Transportation Systems 14, no. 2 (2013): 688-699.

[30]Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." In Computer  Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-I. IEEE, 2001.