Improving performance in small datasets via pre-trained architectures based on VGGFace and VGGFace2 datasets


Algoritmos de reconhecimento facial têm alcançado excelentes resultados sob condições controladas, principalmente por meio de técnicas computacionais de aprendizado profundo. No entanto, o desempenho em condições não controladas ainda precisa ser melhorado. Os sistemas de reconhecimento facial em problemas do mundo real geralmente lidam com condições não controladas, tais como, oclusões e variações de pose e iluminação, que degradam o desempenho do reconhecimento. Apesar dessas limitações, com amostras suficientes de treinamento, ainda é possível alcançar alto desempenho por meio das arquiteturas existentes de aprendizado profundo. No entanto, a falta de amostras de treinamento geralmente resulta em baixa precisão de reconhecimento nesse domínio. Neste estudo, foi demonstrado que a utilização de modelos pré-treinados para a tarefa de reconhecimento facial pode melhorar significativamente o desempenho em cenários com um baixo número de imagens de treinamento disponíveis.


Adjabi, I., Ouahabi, A., Benzaoui, A., and Taleb-Ahmed, A. (2020). Past, present, and future of face recognition: A review. Electronics, 9(8):1188.

Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12):2037–2041.

Al-Raisi, A. N. and Al-Khouri, A. M. (2008). Iris recognition and the challenge of homeland and border control security in uae. Telematics and Informatics, 25(2):117–132.

Astawa, I. N. G. A., Putra, I. K. G. D., Sudarma, M., and Hartati, R. S. (2020). Komnet: Face image dataset from various media for face recognition. Data in brief, 31:105677.

Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7):711–720.

Cao, Q., Shen, L., Xie, W., Parkhi, O. M., and Zisserman, A. (2018). Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pages 67–74. IEEE.

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.

Deng, J., Guo, J., Xue, N., and Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699.

Duan, Q. and Zhang, L. (2020). Look more into occlusion: Realistic face frontalization and recognition with boostgan. IEEE transactions on neural networks and learning systems, 32(1):214–228.

Hasan, M. M., Hossain, M. A., Srizon, A. Y., Sayeed, A., Ahmed, M., and Haquek, M. R. (2021). Improving performance of a pre-trained resnet-50 based vggface recognition system by utilizing retraining as a heuristic step. In 2021 24th International Conference on Computer and Information Technology (ICCIT), pages 1–6. IEEE.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.

Heidari, M. and Fouladi-Ghaleh, K. (2020). Using siamese networks with transfer learning for face recognition on small-samples datasets. In 2020 International Conference on Machine Vision and Image Processing (MVIP), pages 1–4. IEEE.

Hossain, M. I., Kabir, H., et al. (2021). An efficient way to recognize faces using mean embeddings. In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pages 1–10. IEEE.

Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2017). Squeeze-and-excitation networks.

Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst.

Kaur, P., Krishan, K., Sharma, S. K., and Kanchan, T. (2020). Facial-recognition algorithms: A literature review. Medicine, Science and the Law, 60(2):131–139.

Kloss, R. B., Jordao, A., and Schwartz, W. R. (2018). Face verification: Strategies for employing deep models. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 258–262. IEEE.

Kohli, N., Yadav, D., and Noore, A. (2018). Face verification with disguise variations via deep disguise recognizer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 17–24.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

Liu, C. and Wechsler, H. (2002). Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image processing, 11(4):467–476.

Mandal, T., Majumdar, A., and Wu, Q. J. (2007). Face recognition by curvelet based feature extraction. In Image Analysis and Recognition: 4th International Conference, ICIAR 2007, Montreal, Canada, August 22-24, 2007. Proceedings 4, pages 806–817. Springer.

Parkhi, O. M., Vedaldi, A., and Zisserman, A. (2015). Deep face recognition.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252.

Schroff, F., Kalenichenko, D., and Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815–823.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Sirovich, L. and Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3):519–524.

Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701–1708.

Targino, J. M. (2018). Reconstrução de oclusões parciais em imagens de face visando o reconhecimento biométrico. PhD thesis, Universidade de São Paulo.

Trigueros, D. S., Meng, L., and Hartnett, M. (2018). Face recognition: From traditional to deep learning methods. arXiv preprint arXiv:1811.00116.

Wang, H., Kang, B., and Kim, D. (2013). Pfw: A face database in the wild for studying face identification and verification in uncontrolled environment. In 2013 2nd IAPR Asian Conference on Pattern Recognition, pages 356–360. IEEE.

Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W. (2018). Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5265–5274.

Wen, G., Chen, H., Cai, D., and He, X. (2018). Improving face recognition with domain adaptation. Neurocomputing, 287:45–51.

Wolf, L., Hassner, T., and Maoz, I. (2011). Face recognition in unconstrained videos with matched background similarity. In CVPR 2011, pages 529–534. IEEE.

Yu, H., Luo, Z., and Tang, Y. (2016). Transfer learning for face identification with deep face model. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pages 13–18. IEEE.

Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE signal processing letters, 23(10):1499–1503.

Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D. L., and Weng, J. (1998). Discriminant analysis of principal components for face recognition. Face recognition: From theory to applications, pages 73–85.

Zheng, Y., Pal, D. K., and Savvides, M. (2018). Ring loss: Convex feature normalization for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5089–5097.
SIMÕES, Rodolfo; KEMMER, Bruno; IVAMOTO, Victor; LIMA, Clodoaldo. Improving performance in small datasets via pre-trained architectures based on VGGFace and VGGFace2 datasets. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 112-125. ISSN 2763-9061. DOI: