Face Detection with Effective Feature Extraction

TL;DR

This paper explores alternative simple features for face detection beyond Haar-like features, demonstrating that feature co-occurrences enhance performance and generalization in face detection systems.

Contribution

The study introduces effective simple features and a co-occurrence approach that improve face detection accuracy over traditional Haar-like features.

Findings

Proposed features outperform Haar-like features in detection accuracy

Feature co-occurrences improve generalization performance

Features are crucial for system robustness and accuracy

Abstract

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.