Year: 2025
Type: conference-paper
Abstract: Face detection technology is critical in video surveillance applications. Unfortunately, many of the existing approaches fail to detect tiny faces, especially in videos acquired in uncontrolled public environments. In this work, we address this challenge by proposing a novel detection framework that relies on a new multiscale detector that does not require the input to be re-scaled. Our framework leverages existing detectors to detect relatively large faces along with a new detector specifically designed for tiny faces. The core part of our framework is the use of an integral score to detect tiny faces by densely scanning several support regions at different scales. Although this strategy may pose high computational complexity in high-resolution videos, the integral operations used by our framework significantly reduce the computational cost, making it feasible for high-resolution videos and real-time applications. Experiments on the WIDER FACE and MEVA datasets show a significant improvement in performance, particularly for tiny faces depicted in surveillance videos acquired in uncontrolled environments.
Authors: Roberto Leyva, Guodong Shen, Ozan Bahadir, Victor Sanchez, Tanaya Guha
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Defence and Security Accelerator
OpenAlex ID: W4413018608