{"title":"Detection of Eye in a Face Image using Adaboost Algorithm in Comparison with Boosting Algorithm to Measure Accuracy and Sensitivity","authors":"Haranadh Reddy Malepati, S. Premkumar","doi":"10.1109/ICTACS56270.2022.9988734","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTACS56270.2022.9988734","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.