Avaliação do Impacto da Super-Resolução no Reconhecimento Facial em Imagens de Baixa Resolução
Resumo
No Reconhecimento Facial, um dos principais obstáculos é a quantidade baixa de pixels no rosto para a detecção e o reconhecimento facial. Este artigo investiga o impacto de técnicas de Super-Resolução no desempenho de sistemas de reconhecimento facial motivado pelo problema do controle automático de presença em salas de aula através de tecnologias como a RRDBNet para a Super-Resolução, Aprendizado Profundo para a detecção, recorte e alinhamento facial. Tendo como principal base o LFW e o pipeline gerou resultados que demonstram a issociação entre as métricas de reconstrução e o desempenho biométrico.
Referências
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Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision, pages 694–711.
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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 (CVPR), pages 1701–1708. IEEE.
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Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Loy, C. C. (2018). ESRGAN: Enhanced super-resolution generative adversarial networks. In European Conference on Computer Vision Workshops.
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.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press, Cambridge, Massachusetts.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition.
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, University of Massachusetts, Amherst.
InsightFace (2026). Insightface: 2d and 3d face analysis project. [link]. Accessed: 14 Aug. 2026.
Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision, pages 694–711.
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4681–4690.
ONNX Community (2019). Open neural network exchange (onnx). [link]. Accessed: 2026-08-06.
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.
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 (CVPR), pages 1701–1708. IEEE.
Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1):71–86.
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Loy, C. C. (2018). ESRGAN: Enhanced super-resolution generative adversarial networks. In European Conference on Computer Vision Workshops.
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.
Publicado
23/09/2026
Como Citar
LIMA, Artur Vinícius Santos de; CRUZ, João Pedro Ferreira da; MATOS, Leonardo Nogueira.
Avaliação do Impacto da Super-Resolução no Reconhecimento Facial em Imagens de Baixa Resolução. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 26. , 2026, Maragogi/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
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p. 101-110.
DOI: https://doi.org/10.5753/erbase.2026.32069.
