改进的Haar级联算法显著提升门禁人脸识别准确率。
An Enhancement of Haar Cascade Algorithm Applied to Face Recognition for Gate Pass Security
- 引入RGB转换与过滤逻辑，优化人脸特征编码。
- 准确率达98.39%，误检率大幅降低。
- 适合复杂光照、遮挡场景下的门禁安全应用。
本研究针对门禁通行中的人脸识别问题，改进了Haar级联算法以降低误检和漏检率，提升检测精度。通过将级联输出的灰度图转为RGB，增强面部特征向量（128维）编码效果，并加入逻辑筛选流程减少非人脸误检。在包含550张图像的同一数据集上，经301,950次对比测试，改进算法达到98.39%准确率（较原算法提升21.39%）、63.59%精确率、98.30%召回率及72.23%F1分数；原算法则为46.70%~77.00%准确率、44.15%精确率、98.61%召回率与47.01%F1分数。改进算法在复杂背景、光照变化及遮挡情况下表现更优，显著提升人脸识别可靠性。
原文摘要 · Abstract (English)
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.
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