A Mobile App for Age and Gender Identification Using Deep Learning Technique

• Published in: 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) pp. 101 - 107
• Main Authors: Kumar, J.N.V.R. Swarup, Babu, B. Mahesh, Vignesh, Modepalli, Manoi, Pera, Vamsi, T.M.N., Kamal, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.02.2023
• Subjects: Age Estimation; classification; Convolutional Neural Network; Deep learning; Face detection; Gender Acknowledgment; Layout; Lighting; Mobile applications; Multitasking; Predictive models; Security
• DOI: 10.1109/ICISCoIS56541.2023.10100432

Due to its numerous applications in a range of facial research concerns, automated age group and gender estimates from face photos have sparked a lot of attention recently. Regardless, due to considerable intra-class variability in key picture properties, the display models still fall short of the required accuracy requirement for layout in real-world applications (such as size, illumination, placement, and obstacle). This research describes a smartphone application that uses convolutional neural networks to reliably identify a set of face photos' gender and age. By utilizing the consideration element, our model may focus on the essential and enlightening aspects of the face, assisting it in creating more precise anticipation. They utilize a multi-task learning approach to prepare our model and appear that including the anticipated gender to include implanting of the age classifier moves forward the exactness of age forecast. Our model was created employing an ill-known UTK dataset, and it develops a mobile application for predicting a person's age and gender using the proposedmodel.