Data for: Upasthiti: A Feature Learning-inspired Remote Attendance Management System

Published: 8 February 2021| Version 1 | DOI: 10.17632/gcjh52j2j2.1
Samarjit Roy, Satanu Maity, Debashis De


Initially, we have congregated a lot of images of the students and the staff members’ face from our organisation for our experiment. It has been performed using a self-made python program with the help of OpenCV module, which takes the live image of the persons, converts it into grayscale images (for training computation-intensive purpose), and crop the desire face area with the size of 64x64 pixels measurement. And finally, we have accumulated the images in the individual user_name folders, as well as we have created a CSV file for machine training purposes. This CSV file consists of ~10k rows which mean approximately 10k facial images and a total of 4,097 columns, in between 4,096 columns have held the image data, i.e., pixel value from 0-255, and the remaining one contains the IDs of every users.



Computational Pattern Recognition, Image Processing, Facial Recognition