Abstract
Adaboost algorithm is a machine learning for face recognition and using eigenvalues for feature extraction. AdaBoost is also called as an adaptive boost algorithm. To create a strong learner by uses multiple iterations in the AdaBoost algorithm. AdaBoost generates a strong learner by iteratively adding weak learners. To create a strong classifier using several classifiers while training the data set, a new weak learner is adding together, and a weighting vector is adjusting to focus on examples that were misclassifying in prior rounds. The analysis of face recognition has been widely used for any application. As per the literature survey, many algorithms have been developed to recognize the face. The AdaBoost algorithm is used for increasing detection accuracy and easy to develop. Therefore, this paper evaluating a preprocessing, feature extraction by using eigenvalues, classifier for classifying a face or non-face by using an AdaBoost algorithm.
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Thilagavathi, B., Suthendran, K., Srujanraju, K. (2021). Evaluating the AdaBoost Algorithm for Biometric-Based Face Recognition. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S. (eds) Data Engineering and Communication Technology. Lecture Notes on Data Engineering and Communications Technologies, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-16-0081-4_67
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