Face Detection for Low-Light Face in Real-Time Video using VamStack Platform

Face Recognition has become one of the most interesting research topics of Computer Vision for the past decade. It has been applied to various fields such as video surveillance, authentication in a number of platforms. Face Detection is the prior step to detect the face area in the video. Due to the fact that a number of constraints always occur from the low-resolution input videos, it is still a challenging task to significantly improve the performance of Face Detection and even worse for the Face Recognition algorithm. One of the well-known issues that lead most algorithms to misinterpret the faces area happens when the camera is facing to the high-intensity light and the human faces are almost completely black. To overcome such issues, VamStack has developed a number of significant steps to improve the performance of the Face Recognition task. In this paper, we propose an algorithm of Low-Light Face in Real-Time Video Using the VamStack Platform. As a result, the performance of the Face Detection algorithm using VamStack provides the highest accuracy comparing to the existing well-known approaches.