{"title":"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","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":"{\"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}","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}
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.