Optimasi Pipeline Pengenalan Wajah Berbasis Mobile FaceNet menuju Autentikasi yang Liveness (Liveness-Aware)
DOI:
https://doi.org/10.55606/juprit.v5i3.6693Keywords:
Cosine Similarity, Face Recognition, Liveness Detection, MobileFaceNet, On-Device Authentication, React Native, TFLiteAbstract
Bi Biometric face authentication on mobile devices faces two main challenges: computational efficiency under limited resources and vulnerability to spoofing attacks using photos or videos. This study presents an optimized face recognition pipeline by integrating the MobileFaceNet model in TensorFlow Lite (TFLite) format into a React Native application, reinforced with a liveness detection module to ensure the presence of a genuine face. The developed system consists of four main stages: (1) real-time face detection using Expo Face Detector, (2) preprocessing involving cropping and resizing to 112 × 112 pixels with pixel normalization to the range of [−1, 1], (3) MobileFaceNet inference to generate 128-dimensional face embeddings, and (4) verification using cosine similarity with a threshold of 0.75. Initial testing shows that the system operates entirely on-device without relying on external servers, with inference latency suitable for real-time attendance applications. The integration of liveness detection is proposed as an additional layer of defense against presentation attacks. The main contribution of this study is an integrated, lightweight, and secure pipeline architecture that can be implemented on mid-range mobile devices.
Downloads
References
A. George and S. Marcel. (2023). “Effectiveness of Random Deep Feature Selection for Securing Face Presentation Attack Detection Systems,.” In Proc. IEEE ICASSP, 1–5.
Abbeduto, L., Murphy, M. M., Richmond, E. K., Amman, A., Beth, P., Weissman, M. D., Kim, J.-S., Cawthon, S. W., Karadottir, S., Abe, H., Yada, H., Yamamoto, T., Sohma, H., Abraham, S., Perez, P., Adarves-Yorno, I., Jetten, J., Postmes, T., Haslam, S. A., … Elaad, E. (2019). Teacher perceptions of two multi-component interventions: Disability awareness and science. Frontiers in Psychology, 31(2).
Ahmad, F. A., & Pratiwi, N. (2025). Implementasi Pengenalan Wajah, Deteksi Kehadiran, dan Geolokasi menggunakan TensorFlow Lite dan Google ML Kit pada Aplikasi Absensi Mobile . Sistemasi: Jurnal Sistem Informasi, 14(1). https://doi.org/10.32520/stmsi.v14i1.4775
Alauddin, F. D. A., Aman, A., Ghazali, M. F., & Daud, S. (2025). The influence of digital platforms on gig workers: A systematic literature review. Heliyon, 11(1). https://doi.org/10.1016/j.heliyon.2024.e41491
Anggara, K. D., Kartikasari, D. P., & Bakhtiar, F. A. (2023). Implementasi Algoritma MTCNN dalam Mekanisme Autentikasi berbasis Pengenalan Wajah. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(8).
Arkhurst, A. V., Asampanbilla, J. K., & Ametemeh, I. M. (2025). Machine Learning for Personalized Education: Optimizing Learning Outcomes among Diverse Online Learner Populations in Ghana Using Reinforcement Learning. International Journal of Research and Innovation in Social Science, IX(IIIS). https://doi.org/10.47772/ijriss.2025.903sedu0399
Balta, N. (2024). A Short Review of AI in Education: Perspectives from the Web of Science Database. The European Educational Researcher, 7(2). https://doi.org/10.31757/euer.723
Budi, A., Suma’inna, S., & Maulana, H. (2018). Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis (PCA). JURNAL TEKNIK INFORMATIKA, 9(2). https://doi.org/10.15408/jti.v9i2.5608
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J. F., Brañas-Garza, P., Butera, L., Douglas, K. M., Everett, J. A. C., Gigerenzer, G., Greenhow, C., Hashimoto, D. A., Holt-Lunstad, J., Jetten, J., Johnson, S., Kunz, W. H., Longoni, C., … Viale, R. (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus, 3(6). https://doi.org/10.1093/pnasnexus/pgae191
Grzesik, P., & Mrozek, D. (2024). Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases. In Electronics (Switzerland) (Vol. 13, Issue 3). https://doi.org/10.3390/electronics13030640
Hartanto, B., Yudanto, B. W., & Kustanto, K. (2025). Implementasi Google Ml Kit untuk Liveness Detection dalam Sistem Face Recognition: Analisis Kinerja dan Keamanan pada Aplikasi Mobile . Biner : Jurnal Ilmiah Informatika Dan Komputer, 4(1). https://doi.org/10.32699/biner.v4i1.8870
J. Deng, J. Guo, N. Xue, and S. Z. (2019). “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,.” In Proc. IEEE CVPR, 4690–4699. https://doi.org/10.1109/CVPR.2019.00482
Jouini, O., Sethom, K., Namoun, A., Aljohani, N., Alanazi, M. H., & Alanazi, M. N. (2024). A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions. In Technologies (Vol. 12, Issue 6). https://doi.org/10.3390/technologies12060081
Khatina Sari, Jasmir, & Arvita, Y. (2022). Perancangan Sistem Absensi Facial Recognition Menggunakan CNN dan Liveness Detector pada BPR Central Dana Mandiri. Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM), 2(1). https://doi.org/10.33998/jakakom.2022.2.1.63
M. Kim, A. K. Jain, and X. L. (2022). “AdaFace: Quality Adaptive Margin for Face Recognition,.” In Proc. IEEE CVPR, 18729–18738.
Meldyantono, A. P., & Poetro, B. S. W. (2025). Implementasi Sistem Absensi Berbasis Pengenalan Wajah Menggunakan Metode CNN dan Model FaceNet. Jurnal Rekayasa Sistem Informasi Dan Teknologi, 2(3). https://doi.org/10.70248/jrsit.v2i3.1857
Rizki, A. I., Regasari, R., Putri, M., & Shaffan, N. H. (2025). Sistem Pintu Cerdas Berbasis Pengenalan Wajah dan Kartu Identitas Menggunakan YOLOv8 dan Optical Character Recognition (OCR). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 9(4).
S. Meng, J. Zhao, Z. Hu, X. Wang, F. Chen, Y. Jiang, and J. L. (2021). “MagFace: A Universal Representation for Face Recognition and Quality Assessment,.” In Proc. IEEE CVPR, 14225–14234. https://doi.org/10.1109/CVPR46437.2021.01400
Sannigrahi, M., & Roy Chowdhury Ghosh, D. S. (2025). Impact of Evaluating Artificial Intelligence. Interdisciplinary Perspectives of Education, 2(2). https://doi.org/10.65525/ipe.v2i2.20
Vera Wati, Yuliana, Nisrina Yulia Setyowati, & Mudawil Qulub. (2023). Deteksi Wajah menggunakan Algoritma Viola Jones Berbasis Android. Teknimedia: Teknologi Informasi Dan Multimedia, 4(1). https://doi.org/10.46764/teknimedia.v4i1.92
Wang, R., Li, F., & Paulussen, J. (2010). Conjugate ecopolis planning: Balancing ecoservice and human well-being in Beijing. In Annals of the New York Academy of Sciences (Vol. 1195, Issue SUPPL. 1). https://doi.org/10.1111/j.1749-6632.2009.05406.x
Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications.
Zhang, Q. (2025). The role of EFL teacher immediacy and teacher-student rapport in boosting motivation to learn and academic mindsets in online education. Learning and Motivation, 89, 102092. https://doi.org/https://doi.org/10.1016/j.lmot.2024.102092
Zhang, Y., & Luo, S. (2022). Connecting EFL curriculum reforms with teacher and student learning: insights from two lesson studies. International Journal for Lesson and Learning Studies, 11(4). https://doi.org/10.1108/IJLLS-06-2022-0073
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Danuri Danuri, Kusrini Kusrini
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
