Age and Gender Prediction

Abstract

Abstract: The method of classifying facial photos or videos into distinct age groups, known as age group classification, is crucial for a variety of industries, including recruitment, security, healthcare, and intelligent social robots. This article offers a thorough methodology for classifying age groups. In order to prepare input photos for feature extraction using deep convolutional neural networks (DCNN), the methodology first preprocesses the images. The DCNN takes the source face image and extracts D-dimensional features from it. A hybrid particle swarm optimization (HPSO) technique is used to choose facial features in order to increase the distinctiveness and recognizability of facial features. The Support Vector Machine (SVM) is then used to categorize the data by age and gender. The importance of these age and gender categories in a dietary recommendation system exemplifies how this research can be used in real-world settings. The system's performance is evaluated using real-world photos, and the results show excellent results in terms of prediction accuracy and computing efficiency. Applying evaluation measures to datasets like Adience and UTKface, such as classification rate, precision, and recall, further validates the effectiveness of the suggested method.