A Road map for Age and Gender Classification using EL-Methods

Age and Gender analysis is very crucial to prediction for blind people. This variety of predictions is particularly used for blind people. Gender and age classification relies on distinct attributes for their determination. The gender classification with the help of facial expression features, height, and body shape. Similarly, age classification is performed with the help of wrinkles, skin texture, and facial features. Different ML method are used to predict gender and age classification. In this work put forward, an EL-based machine learning algorithm is in use to anticipate and age classification. The SVM algorithm is used for the grouping of gender and age grouping. Based on the boosting ensemble learning with the help of SVM, performed better classification of age and genderprediction. This novelty method has been incorporated into ensemble learning with implemented using 1056 images with different pixel intensity values after prepossessing all images get into the same, and based on that, training and testing are evaluated at 80% and 20%. The proposed ensemble method produced better accuracy of age (97%) and gender (96%) grouping.