Detection of eye in a face image using Adaboost algorithm in comparison with boosting algorithm to measure accuracy and sensitivity

The novel adaboost method for accurate eye recognition in face photographs is the focus of this research project, which aims to implement said approach. The purpose of this investigation is to evaluate how well the boosting algorithm performs in comparison to the newly developed Adaboost method. Images of human faces were extracted from the ORL Face Database and analysed for characteristics that may be used for eye recognition. In this study, we employ a total of forty people: twenty from each of two groups. In the simulations, the pretest power was established at 0.8. In order to evaluate how effectively the innovative Adaboost algorithm works, metrics such as accuracy and sensitivity are produced and analysed. The Adaboost Algorithm has an accuracy of 98.73 percent, while the Boosting Algorithm has an accuracy of 86.41 percent, and the Sensitivity of the Adaboost Algorithm is 96.15 percent, while the Sensitivity of the Boosting Algorithm is only 83.63 percent. These results come from simulations run with SPSS. In the case of this model, we discover that the level of significance is 0.000. (2-tailed, p0.05). In this study, it is shown that the unique Adaboost method is superior to the boosting algorithm in terms of accuracy and sensitivity.