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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Robotics and Control Systems Jurnal Ilmiah Edutic : Pendidikan dan Informatika SainsTech Innovation Journal
Sa'adati, Yuan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Journal : International Journal of Robotics and Control Systems

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Cross-Age Face Verification Using Generative Adversarial Networks (GAN) with Landmark Feature Syuhada, Fahmi; Sa'adati, Yuan
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1755

Abstract

Cross-age face verification is a complex problem in biometric recognition in terms of aging, a naturally changing face structure, and face landmark configuration changes over time. In this paper, a new cross-age face verification method is proposed with a Generative Adversarial Network (GAN) and a mix of landmark-based features. Realistic aging of a face with identity-specific landmarks, such as eyes, nose, and mouth, is generated for effective face recognition in a range of age groups. Performance testing with an in-house collected face dataset of 200 face images exhibited effectiveness in changing face configuration and face shape transformations, such as a fuller face thinning and thin face becoming fuller. Comparison with direct face verification showed increased values of similarity, such as 32.57% to 63.80%, reduced values of feature distance, such as 0.6743 to 0.3620, and improvement in accuracy for the ArcFace, VGG-Face, and Facenet architectures. ArcFace exhibited an improvement in accuracy with an increase in value from 82.64% to 86.02%, VGG-Face with an improvement in value from 76.23% to 80.57%, and Facenet with an improvement in value from 67.54% to 74.48%. These observations validate the effectiveness of the proposed method in overcoming age-related complications and improving cross-age face verification performance. In future work, we plan to investigate a larger dataset and model refinement to realize performance improvement and real-life biometric suitability.
Co-Authors Ahmad Fatoni Dwi Putra Ali, Haerul Azwar Anwariah, Komariyuli Barliansyah R, Ferdy Fadli, Sofiansyah Fahmi Syuhada Fahmi Syuhada Fahmi, Hairul Fawaid, Haekal Hidayatullah, Zahid Khairul Imtihan Komariyuli Anwariyah Pardiansyah, Ahmad S. Praja, Winata Pratama, Ramadhana Agung Saikin Saleh, Maemun Syahrani Lonang Syuhada, Fahmi