Implementation of FaceNet for Facial Search Function in Reverse Image Search System

Muhammad Rayhan Rachmansyah; Basuki Rahmat; Henni Endah

doi:10.54209/jatilima.v7i04.1880

Muhammad Rayhan Rachmansyah Universitas Pembangunan Nasional Veteran Jawa Timur
Basuki Rahmat Universitas Pembangunan Nasional Veteran Jawa Timur
Henni Endah Universitas Pembangunan Nasional Veteran Jawa Timur

DOI: https://doi.org/10.54209/jatilima.v7i04.1880

Keywords: FaceNet, Deep Convolutional Neural Network, Face Recognition, Reverse Image Search

Article Metrics

Abstract view : 97 times

Abstract

This study presents the implementation of the FaceNet deep convolutional neural network model as a facial search function for a reverse image search system. The research addresses the growing need for fast and accurate face recognition in digital applications, focusing on embedding-based similarity search rather than traditional classification methods. The system employs MTCNN for face detection and alignment, followed by the FaceNet model to generate 128-dimensional facial embeddings whose distances represent the similarity between identities. The methodology includes data preprocessing, embedding extraction, distance-based matching, and system evaluation using images with identical and non-identical identities. Experimental results show that the average embedding distance for identical faces is 0.47, while non-identical faces exhibit an average distance of 0.62. The proposed system achieves an accuracy of 94% on a test set of 100 images, demonstrating its effectiveness in distinguishing facial similarities. These findings confirm that embedding-based representation using FaceNet provides a reliable foundation for facial retrieval tasks in reverse image search applications, offering high discriminative capability and operational efficiency.

References

Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, present, and future of face recognition: A review. In Electronics (Switzerland) (Vol. 9, Issue 8, pp. 1–53). MDPI AG. https://doi.org/10.3390/electronics9081188

Astawa, I. N. G. A., Radhitya, M. L., Ardana, I. W. R., & Dwiyanto, F. A. (2021). Face Images Classification using VGG-CNN. Knowledge Engineering and Data Science, 4(1), 49. https://doi.org/10.17977/um018v4i12021p49-54

Ben Fredj, H., Bouguezzi, S., & Souani, C. (2021). Face recognition in unconstrained environment with CNN. Visual Computer, 37(2), 217–226. https://doi.org/10.1007/s00371-020-01794-9

Bengio, Y., Lecun, Y., & Hinton, G. (2021). Deep learning for AI. Communications of the ACM, 64(7), 58–65. https://doi.org/10.1145/3448250

Catoer Ryando, Riyanto Sigit, Setiawardhana, & Bima Sena Bayu Dewantara. (2024). Comparison of Machine Learning Algorithms for Face Classification Using FaceNet Embeddings. The Indonesian Journal of Computer Science, 13(4). https://doi.org/10.33022/ijcs.v13i4.4323

Challapalli, S. S. A. (2024). Profile Face Recognition and Classification Using Multi-Task CascadedConvolutional Networks. Journal of Computer Allied Intelligence, 2(6), 65–78. https://doi.org/10.69996/jcai.2024029

Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. In Remote Sensing (Vol. 13, Issue 22). MDPI. https://doi.org/10.3390/rs13224712

Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., & Zafeiriou, S. (2022). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44. https://doi.org/10.1109/TPAMI.2021.3087709

Lin, W., Chen, J., Mei, J., Coca, A., & Byrne, B. (2023). Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (pp. 22820–22840). https://arxiv.org/abs/2309.17133

Liu, F., Chen, D., Wang, F., Li, Z., & Xu, F. (2023). Deep learning based single sample face recognition: a survey. Artificial Intelligence Review, 56(3), 2723–2748. https://doi.org/10.1007/s10462-022-10240-2

McNeely-White, D., Beveridge, J. R., & Draper, B. A. (2020). Inception and ResNet features are (almost) equivalent. Cognitive Systems Research, 59, 312–318. https://doi.org/10.1016/j.cogsys.2019.10.004

Meng, Q., Zhao, S., Huang, Z., & Zhou, F. (2021). MagFace: A Universal Representation for Face Recognition and Quality Assessment. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14225–14234. http://arxiv.org/abs/2103.06627

Wei, X.-S., Song, Y.-Z., Mac Aodha, O., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2021). Fine-Grained Image Analysis with Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 8927–8948. http://arxiv.org/abs/2111.06119

Yuan, Y., Chen, W., Yang, Y., & Wang, Z. (2020). In defense of the triplet loss again: Learning robust person re-identification with fast approximated triplet loss and label distillation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 1454–1463. https://doi.org/10.1109/CVPRW50498.2020.00185

Zhang, D., Li, Y., & Zhang, Z. (2020). Deep Metric Learning with Spherical Embedding. Advances in Neural Information Processing Systems, 33, 18772–18783. http://arxiv.org/abs/2011.02785

Zhang, F., Li, H., Qian, S., Wang, X., Lian, Z., Wu, H., Zhu, Z., Gao, Y., Li, Q., Zheng, Y., Lin, Z., & Heng, P.-A. (2025). Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond. http://arxiv.org/abs/2511.00389

Zhu, Z., Huang, G., Deng, J., Ye, Y., Huang, J., Chen, X., Zhu, J., Yang, T., Lu, J., Du, D., & Zhou, J. (2021). WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10487–10497. https://doi.org/10.1109/CVPR46437.2021.01035