A Framework for Fast Face and Eye Detection

TL;DR

This paper proposes a faster face and eye detection framework that enhances the Haar-like features method by down sampling frames and using affine transformations to detect tilted faces, improving speed and robustness.

Contribution

It introduces modifications to the Haar-like features approach, including frame down sampling and affine transformations, to improve detection speed and handle tilted faces effectively.

Findings

Increased detection speed through frame down sampling.

Effective detection of tilted faces using affine transformations.

Enhanced robustness of face detection in various orientations.

Abstract

Face detection is an essential step in many computer vision applications like surveillance, tracking, medical analysis, facial expression analysis etc. Several approaches have been made in the direction of face detection. Among them, Haar-like features based method is a robust method. In spite of the robustness, Haar - like features work with some limitations. However, with some simple modifications in the algorithm, its performance can be made faster and more robust. The present work refers to the increase in speed of operation of the original algorithm by down sampling the frames and its analysis with different scale factors. It also discusses the detection of tilted faces using an affine transformation of the input image.