KYHTQT.

A Lightweight Attention Model for Face Recognition

Năm XB 2023 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 848 Đơn vị NT&TT DOI / Link https://doi.org/10.1007/978-3-031-50818-9_25 ↗

Tác giả

Tóm tắt

Facial recognition is one of the most popular biometric methods in the real world. To address this task, various methods have been proposed especially deep learning-based approaches. However, most of them usually focus on improving performance by building deeper and more complex networks. This makes an important limitation for the ability to deploy them on embedded or mobile devices without GPU. In this paper, we introduce an efficient network called SeesawAttentionFaceNet which is a hybrid of SeesawFaceNet and CBAM attention to improve the performance of face recognition while keeping the simple to suitable for edge devices. The experiments conducted on various datasets have shown that our proposed framework outperforms the other state-of-the-art lightweight models for facial recognition. Moreover, we also provide an ablation study to demonstrate that when the face area of the removed position increases, the recognition results typically are reduced. This has important implications in feature extraction and processing of facial images.

Tài liệu tham khảo

[1] Chen, S., Liu, Y., Gao, X., Han, Z.: Mobilefacenets: efficient CNNs for accurate real-time face verification on mobile devices. In: Biometric Recognition: 13th Chinese Conference, CCBR 2018, Urumqi, China, August 11–12, 2018, Proceedings 13, pp. 428–438. Springer (2018)

[2] Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690–4699 (2019)

[3] Duc, Q.V., Phung, T., Nguyen, M., Nguyen, B.Y., Nguyen, T.H.: Self-knowledge distillation: an efficient approach for falling detection. In: International Conference on Artificial Intelligence and Big Data in Digital Era, pp. 369–380. Springer (2021)

[4] Duong, C.N., Luu, K., Quach, K.G., Bui, T.D.: Deep appearance models: a deep Boltzmann machine approach for face modeling. Int. J. Comput. Vis. 127, 437–455 (2019)

[5] Duong, C.N., Quach, K.G., Jalata, I., Le, N., Luu, K.: Mobiface: a lightweight deep learning face recognition on mobile devices. In: BTAS, pp. 1–6. IEEE (2019)

[6] Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

[7] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

[8] Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

[9] Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008)

[10] Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4873–4882 (2016)

[11] Kim, M., Jain, A.K., Liu, X.: Adaface: quality adaptive margin for face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18750–18759 (2022)

[12] Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: Agedb: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–59 (2017)

[13] Nhan Duong, C., Luu, K., Gia Quach, K., Bui, T.D.: Longitudinal face modeling via temporal deep restricted Boltzmann machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5772–5780 (2016)

[14] Phung, T., Nguyen, V.T., Ma, T.H.T., Duc, Q.V.: A (2+ 1) d attention convolutional neural network for video prediction. In: International Conference on Artificial Intelligence and Big Data in Digital Era, pp. 395–406. Springer (2021)

[15] Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9. IEEE (2016)

[16] Tan, H.M., Vu, D.Q., Lee, C.T., Li, Y.H., Wang, J.C.: Selective mutual learning: an efficient approach for single channel speech separation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3678–3682. IEEE (2022)

[17] Tan, H.M., Vu, D.Q., Wang, J.C.: Selinet: a lightweight model for single channel speech separation. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)

[18] Vu, D.Q., Le, N., Wang, J.C.: Teaching yourself: a self-knowledge distillation approach to action recognition. IEEE Access 9, 105711–105723 (2021)

[19] Vu, D.Q., Thu, T.P.T.: Simultaneous context and motion learning in video prediction. Signal, Image Video Process. 1–10 (2023)

[20] Wilmer, J.B.: Individual differences in face recognition: a decade of discovery. Curr. Dir. Psychol. Sci. 26(3), 225–230 (2017)

[21] Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

[22] Zhang, J.: Seesaw-net: convolution neural network with uneven group convolution. arXiv:1905.03672 (2019)

[23] Zhang, J.: Seesawfacenets: sparse and robust face verification model for mobile platform. arXiv:1908.09124 (2019)

Ghi chú

ICTA
← Quay lại danh sách bài báo