@article{Gururaj_Batra_2021, title={InnovFaceNet: Deep Face Recognition for Industrial Environments}, volume={6}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/3553}, DOI={10.15353/jcvis.v6i1.3553}, abstractNote={<p>In recent times the usage of intelligent systems have paved way for<br>many applications to be robust and self-reliant. One such popular<br>and vast growing technology is face recognition. Facial Recognition<br>technology is used in security, surveillance, criminal justice systems<br>and many other multimedia platforms. This work proposes a real<br>time facial recognition technology which can be used in any industrial<br>setup eliminating manual supervision, ensuring authorized access<br>to the personnel in the plant. Due to the recent development of<br>COVID-19 pandemic around the world, wearing masks has become<br>a necessity. Our proposed facial recognition technology identifies a<br>person’s face with mask or no mask in real time with a speed of<br>20 FPS on a CPU and an F1-score of 95.07%. This makes our<br>algorithm fast, secure, robust and deployable on a simple personal<br>computer or any edge device at any industrial plant or organization.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Gururaj, Nagarjun and Batra, Kanika}, year={2021}, month={Jan.}, pages={1–4} }