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Title |
Age Classification using Different Algorithms of Deep Learning and Computer Vision |
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Author |
Amal Alshahrani, Rital Shafiei, Dana Alghamdi, Rahaf Algethami, Shahd Althobaiti, Wafaa Almazmomi |
| Citation |
Vol. 25 No. 6 pp. 45-52 |
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Abstract |
The creation of an AI model that reliably classifies age groups based on people's pictures is suggested in this research study. The recommended approach makes use of the YOLO object detection system in combination with the VGG16 Convolutional Neural Network (CNN) architecture. The AI model is trained using a sizable collection of facial photos with matching age categories, allowing it to discover unique patterns and characteristics unique to various age groups. VGG16 and YOLO are integrated into the training process, utilizing their own strengths in object detection and image categorization. The research's conclusions are important for fields including targeted marketing, age-based demographics, and facial recognition. Using VGG16 and YOLO in the AI model to accurately classify users' ages can improve decision-making, targeted advertising, and user experience in general. The objective of the comparative examination of the outcomes produced by various methods is to offer insightful information about their individual capacities. |
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Keywords |
Age Classification, Convolutional neural network (CNN), YOLOv8, YOLOv5, VGG16. |
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