Abstract
Abstract: The process of categorizing facial pictures or videos into specified age groups is known as age group classification. Due to its many applications, including in recruitment, security, health, and social robots with intelligence, it is a crucial task. Before executing feature extraction using the deep convolution neural network (DCNN) technique, the developed methodology preprocesses the input image. Following the feature selection process, this network extracts D-dimensional characteristics from the source face image. A hybrid particle swarm optimisation (HPSO) method is used to choose the features that make the face distinctive and recognisable. Age and gender are categorised by Support Vector Machine (SVM). The age and gender categories are used in the nutrition recommendation system. Real-world images demonstrate excellent performance by achieving good prediction results and computation time, and the suggested system performs exceptionally well when tested using classification rate, precision, and recall using the Adience dataset and UTKface dataset.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
