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.

翻译：本研究重点在于增强Haar Cascade算法，以降低人脸匹配与人脸检测中的误报率和漏报率，从而在复杂条件下仍能提升准确率。人脸识别库采用Haar Cascade算法实现，该算法可将表征面部独特特征的128维向量进行编码。通过引入子处理流程，将Haar Cascade生成的灰度图像转换为RGB格式以优化面部编码。同时采用逻辑处理与人脸过滤机制以减少非人脸误检。增强型Haar Cascade算法取得了98.39%的准确率（提升21.39%）、63.59%的精确率、98.30%的召回率以及72.23%的F1分数。相比之下，原始Haar Cascade算法的准确率在46.70%至77.00%之间，精确率为44.15%，召回率为98.61%，F1分数为47.01%。两种算法均采用混淆矩阵测试，使用包含550张图像的相同数据集进行了301,950次比对。98.39%的准确率表明该算法显著降低了人脸识别中的误报与漏报率。在复杂背景、光照变化、遮挡甚至具有相似属性的人脸图像中，该算法的人脸匹配与检测均表现出更高的准确性。