用Adaboost算法检测人脸图像中的眼睛,并与boost算法进行比较,以衡量其准确性和灵敏度

Haranadh Reddy Malepati, S. Premkumar
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引用次数: 0

摘要

这项工作的目标是使用Novel adaboost算法可靠地识别人脸图像中的眼睛。以评估独特的Adaboost算法的性能,并将其与增强方法进行对比。材料和程序从ORL人脸数据库中收集人脸图片以识别眼睛。在本次调查中,两组各使用20人作为样本量。仿真中使用的预测功率为0.8。创新Adaboost算法的性能是使用精度和灵敏度值等性能指标来衡量的。结果:Adaboost算法的准确率为98.73%,Boosting算法的准确率为86.41%。Adaboost算法的灵敏度也达到了98.73%。模型显著性值为0.000(双尾)(p 0.05)。在本研究中,我们发现独特的Adaboost方法在准确率和灵敏度上都明显优于boosting算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Eye in a Face Image using Adaboost Algorithm in Comparison with Boosting Algorithm to Measure Accuracy and Sensitivity
The goal of this work is to reliably identify eyes in a face image using the Novel adaboost algorithm. to assess the unique Adaboost algorithm's performance and contrast it with the boosting approach. Materials and Procedures Human face pictures from the ORL Face Database were gathered in order to identify the eye. Twenty people are used as the sample size for each of the two groups in this investigation. The pretest power used in the simulation was 0.8. The performance of the innovative Adaboost algorithm is measured using performance measures like accuracy and sensitivity values. Results: The Adaboost Algorithm has an accuracy of 98.73%, whereas the Boosting Algorithm has an accuracy of 86.41%. The Adaboost Algorithm also has a sensitivity of 98.73%. The model's significance value is 0.000 (2-tailed) (p 0.05). In this study, it was discovered that the unique Adaboost method outperformed the boosting algorithm significantly in terms of accuracy and sensitivity.
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