Age And Gender Recognition Mobile App

• Main Author: Wee, Quo Lung
• Format: Final Year Project Report
• Language: English; English
• Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
• Subjects: QA Mathematics; QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/44118/1/Wee%20Quo%20Lung%20%2824pgs%29.pdf; http://ir.unimas.my/id/eprint/44118/4/Wee%20Quo%20Lung%20ft.pdf; http://ir.unimas.my/id/eprint/44118/

Through reviewing and evaluate the existing age and gender recognition mobile apps and their deep learning algorithm, the study found the number of existing age and gender recognition mobile app is very less. This indicates that only a few developers focusing on developing the age and gender recognition mobile app. In addition, the User Interface (UI) of the existing mobile app is unappealing. Therefore, this study aimed to develop age and gender recognition mobile application using deep learning algorithm. After reviewing existing age and gender recognition mobile app, Convolutional Neural Network (CNN), one of the deep learning algorithms is implement in this proposed system. The CNN model is trained by using UTKFace face dataset which contains 20,000 face images with annotations of age, gender, and ethnicity. In addition, the app utilizes CNN to analyse facial features and other visual cues to make its predictions. The functionality of this proposed mobile app is to allow user to upload photo from gallery. The user simply needs select one image from the gallery, and the app will predict and display the age and gender of the person in the image.