An Enhancement of Haar Cascade Algorithm Applied to Face Recognition for Gate Pass Security

TL;DR

This paper presents an enhanced Haar Cascade Algorithm that significantly improves face recognition accuracy and reduces false positives and negatives, especially under challenging conditions, for gate pass security applications.

Contribution

The study introduces modifications to the Haar Cascade Algorithm, including face encoding improvements and image preprocessing, achieving higher accuracy and robustness in face recognition tasks.

Findings

Achieved 98.39% accuracy rate with enhancement

Reduced false positive and false negative rates

Improved face detection in complex conditions

Abstract

This study is focused on enhancing the Haar Cascade Algorithm to decrease the false positive and false negative rate in face matching and face detection to increase the accuracy rate even under challenging conditions. The face recognition library was implemented with Haar Cascade Algorithm in which the 128-dimensional vectors representing the unique features of a face are encoded. A subprocess was applied where the grayscale image from Haar Cascade was converted to RGB to improve the face encoding. Logical process and face filtering are also used to decrease non-face detection. The Enhanced Haar Cascade Algorithm produced a 98.39% accuracy rate (21.39% increase), 63.59% precision rate, 98.30% recall rate, and 72.23% in F1 Score. In comparison, the Haar Cascade Algorithm achieved a 46.70% to 77.00% accuracy rate, 44.15% precision rate, 98.61% recall rate, and 47.01% in F1 Score. Both algorithms used the Confusion Matrix Test with 301,950 comparisons using the same dataset of 550 images. The 98.39% accuracy rate shows a significant decrease in false positive and false negative rates in facial recognition. Face matching and face detection are more accurate in images with complex backgrounds, lighting variations, and occlusions, or even those with similar attributes.