Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory

Rayendra Tawakal; Muhammad Nazar; Rahmadi Asri

doi:10.29103/jacka.v1i4.18889

Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory

Rayendra Tawakal, Muhammad Nazar, Rahmadi Asri

Abstract

A real-time facial detection system for identifying young and old faces has been developed using a combination of Template Matching and Fuzzy Associative Memory (FAM) methods. This study aims to improve accuracy in detecting facial age, particularly from images captured via a webcam. The system was tested across four categories: Old Men, Young Men, Old Women, and Young Women, with 10 image samples per category. The results indicate that the system achieved an accuracy rate of 83%. The Young Men category exhibited the best performance with 100% accuracy, while detection errors occurred in the Old Men and Old Women categories, with a false positive rate of 30%. The system proved to be more effective at detecting young faces than old faces. The primary challenge of this study was managing the complex variation in the patterns of older faces. Thus, further research is required to enhance the system’s performance in detecting older faces and reduce the false positive rate.

Keywords

Face detection; Template Matching; Fuzzy Associative Memory; Real-time; Young and old faces

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DOI: https://doi.org/10.29103/jacka.v1i4.18889