Founded in 1993, Independent University, Bangladesh (IUB) is one of the oldest and leading private universities in Bangladesh where academic excellence is a tradition, teaching a passion and lifelong learning a habit. IUB currently has more than 9,800 undergraduate and graduate students and over 13,700 alumni. The students of IUB experience an exciting academic life with copious opportunities to explore and nurture their innate talent.
Live in Field Experience (LFE) is a signature course of IUB. It allows IUB students, most of whom come from urban settings, to have an immersive experience of everyday life in rural Bangladesh. Unique in Bangladesh, the overarching idea, which owes its roots to some of the leading social thinkers of this region, is to bridge the gap in knowledge that an urban student has about their rural counterparts. As part of the LFE, which is a mandatory course, small groups of students (usually 5-10, mix of male and female, and English and Bangla medium backgrounds) spend a certain amount of time at different locations in Bangladesh and experience life in the fields.
The team comprises IUB Computer Science and Engineering (CSE) alumni Anik Alvi, currently a graduate student at New Mexico State University (NMSU); Sabir Saheel, currently pursuing higher education at the University of Minnesota; and Aninda Roy; and Associate Professors Tarem Ahmed, PhD, and Faisal Uddin, PhD. This research work has been supported by the RIoT Research Center, Independent University, Bangladesh.
The research, driven by the complexities of surveillance in a world accustomed to mask-wearing during the pandemic, introduces an open-source autonomous surveillance system, marking a notable shift in the accessibility and efficiency of such technologies.
Alvi, Saheel and Roy began this project as their undergraduate thesis at IUB under the supervision of Dr. Ahmed and Dr. Uddin. The project was further developed during Alvi’s tenure as a graduate assistant at the New Mexico Water Resources Research Institute (WRRI) with contributions from his former classmates and supervisors.
The paper details the use of Multi-task Cascaded Convolutional Neural Networks (MTCNN) for facial feature detection, combined with a Gabor image feature extractor and a Kernel-based Online Anomaly Detection (KOAD) algorithm. This ensemble of technologies enables real-time identification of potential risks, enhancing security measures in various public and private settings. The research was rigorously tested across multiple datasets, including two from public online repositories. The results showed a remarkable 78% accuracy in mask detection, outperforming comparable algorithms.
Dr. Faisal Uddin expressed his gratitude towards IUB, especially the Vice Chancellor, for allowing the use of video data from the university's surveillance cameras, which played a crucial role in enhancing the real-world applicability of their research.