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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Tanvir Fatima Naik Bukht1, Naif Al Mudawi2, Saud S. Alotaibi3, Abdulwahab Alazeb2, Mohammed Alonazi4, Aisha Ahmed AlArfaj5, Ahmad Jalal1, Jaekwang Kim6,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
3 Information Systems Department, Umm Al-Qura University, Makkah, Saudi Arabia
4 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Convergence Program for Social Innovation, Sungkyunkwan University, Suwon, 03063, Korea

* Corresponding Author: Jaekwang Kim. Email: email

(This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)

Computers, Materials & Continua 2023, 77(2), 1557-1573.


Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames, making them more vibrant and visually appealing, especially in low-contrast or washed-out scenes. Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method. Feature extraction uses the features from Accelerated Segment Test (FAST), Oriented FAST, and Rotated BRIEF (ORB) techniques. The application of Quadratic Discriminant Analysis (QDA) for feature fusion and discrimination enables high-dimensional data to be effectively analyzed, thus further enhancing the classification process. It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities. The impressive accuracy rates of 93% and 94.6% achieved in the BIT-Interaction and UT-Interaction datasets respectively, highlight the success and reliability of the proposed technique. The proposed approach addresses challenges in various domains by focusing on frame improvement, silhouette and feature extraction, feature fusion, and HMM classification. This enhances data quality, accuracy, adaptability, reliability, and reduction of errors.


Cite This Article

T. F. N. Bukht, N. A. Mudawi, S. S. Alotaibi, A. Alazeb, M. Alonazi et al., "A novel human interaction framework using quadratic discriminant analysis with hmm," Computers, Materials & Continua, vol. 77, no.2, pp. 1557–1573, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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