Abstract
In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep learning systems. A low-cost system is intended to be constructed for recognizing masked faces and compares its performance to that of face recognition systems that do not use masks. The proposed method fuses hand-crafted methods using feature-level fusion strategy. This study compares the performance of masked and unmasked face recognition systems. The experiments are undertaken on two publicly accessible datasets for masked face recognition: Masked Labeled Faces in the Wild (MLFW) and Cross-Age Labeled Faces in the Wild (CALFW). The best accuracy is achieved as 94.8% on MLFW dataset. The rest of the results on different train and test sets from CALFW and MLFW datasets are encouraging compared to the state-of-the-art models.
Supporting Institution
Not available.
References
- Ahamed, H., Alam, I. and Islam, M. M. (2018), HOG-CNN Based Real Time Face Recognition, 2018 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 2018, pp. 1-4, doi: 10.1109/ICAEEE.2018.8642989.
- Cao, Q., Shen, L., Xie, W., Parkhi, O. M. and Zisserman, A. (2018), VGGFace2: A Dataset for Recognising Faces across Pose and Age, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi'an, China, 2018, pp. 67-74, doi: 10.1109/FG.2018.00020.
- Deng, J., Guo, J., Xue, N. and Zafeiriou, S. (2019), ArcFace: Additive Angular Margin Loss for Deep Face Recognition, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 4685-4694, doi: 10.1109/CVPR.2019.00482.
- Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J. (2016), MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition, Sep. 17, 2016. https://link.springer.com/chapter/10.1007/978-3-319-46487-9-6
- Huang, Y. et al. (2020), CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition, 2020 IEEE/CVF Conference on Computer Vision andPattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 5900-5909, doi: 10.1109/CVPR42600.2020.00594.
- Kulkarni, O. S., Deokar, S. M., Chaudhari, A. K., Patankar, S. S. and Kulkarni, J. V. (2017), Real Time Face Recognition Using LBP Features, 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017, pp. 1-5, doi: 10.1109/ICCUBEA.2017.8463886.
- Rani, G. E., Suresh, S. M, M. P., Abhiram, M., Surya, K. J. and Kumar, B. Y. A. N. (2022), Face Recognition Using Principal Component Analysis, 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 932-936, doi: 10.1109/ICACITE53722.2022.9823434.
- Wang, C., Fang, H., Zhong, Y., and Deng, W. (2022), MLFW: A Database for Face Recognition on Masked Faces, In: et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. 2022. doi: 10.1007/978-3-031-20233-9-18.
- Wang, Q., Zhang, P., Xiong, H., and Zhao, J. (2021), Face.evoLVe: A High-Performance Face Recognition Library, arXiv.org, Jul. 19, 2021. https://arxiv.org/abs/2107.08621v4
- Yi, D., Lei, Z., Liao, S., and Li, S. Z. (2014), Learning Face Representation from Scratch, arXiv.org, Nov. 28, 2014. https://arxiv.org/abs/1411.7923v1
- Zheng, T., Deng, W., and Hu, J. (2017), Cross-age LFW: A database for studying cross-age face recognition in unconstrained environments, CoRR, vol. abs/1708.08197, 2017.
- Zhong, Y., Deng, W., Hu, J., Zhao, D., Li, X. and Wen, D. (2021), SFace: Sigmoid- Constrained Hypersphere Loss for Robust Face Recognition, in IEEE Transactions on Image Processing, vol. 30, pp. 2587-2598, 2021, doi: 10.1109/TIP.2020.3048632.
Abstract
Bu çalışmada, maskeli ve maskesiz yüzlerde el yapımı yöntemler kullanılarak yüz tanıma görevi uygulanmıştır. COVID-19 ve maskeler nedeniyle, kısıtlanmamış görüntülerden yüz tanıma önemli bir konu haline gelmiştir. COVID-19'dan kaçınmak için çoğu insan dışarıda maske kullanmaktadır. Birçok durumda, tipik yüz tanıma teknolojisi işe yaramaz. Çoğu çağdaş ileri yüz tanıma yöntemi derin öğrenmeye dayanır ve büyük ölçüde birçok eğitim örneğine dayanır, ancak maske takılmış yüz tanıma, derin öğrenme sistemlerini kullanmaktan daha düşük bir hesaplama maliyeti ile el yapımı yaklaşımlar kullanılarak araştırılabilir. Maske takılmış yüzleri tanımak için düşük maliyetli bir sistem oluşturmak ve maske kullanmayan yüz tanıma sistemlerinin performansını karşılaştırma amaçlanmıştır. Önerilen yöntem, öznitelik düzeyi kaynaşım stratejisi kullanarak el yapımı yöntemleri birleştirir. Bu çalışma, maske takılmış ve takılmamış yüz tanıma sistemlerinin performansını karşılaştırmaktadır. Deneyler, maskeli yüz tanıma için erişime açık iki veri kümesi Masked Labeled Faces in the Wild (MLFW) ve Cross-Age Labeled Faces in the Wild (CALFW) üzerinde gerçekleştirilmiştir. En iyi doğruluk oranı, MLFW veri kümesinde %94,8 olarak elde edilmiştir. CALFW ve MLFW veri kümelerinden farklı eğitim ve test kümeleri kullanılarak elde edilen diğer sonuçlar, mevcut en iyi modellere göre cesaret vericidir.
References
- Ahamed, H., Alam, I. and Islam, M. M. (2018), HOG-CNN Based Real Time Face Recognition, 2018 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 2018, pp. 1-4, doi: 10.1109/ICAEEE.2018.8642989.
- Cao, Q., Shen, L., Xie, W., Parkhi, O. M. and Zisserman, A. (2018), VGGFace2: A Dataset for Recognising Faces across Pose and Age, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi'an, China, 2018, pp. 67-74, doi: 10.1109/FG.2018.00020.
- Deng, J., Guo, J., Xue, N. and Zafeiriou, S. (2019), ArcFace: Additive Angular Margin Loss for Deep Face Recognition, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 4685-4694, doi: 10.1109/CVPR.2019.00482.
- Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J. (2016), MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition, Sep. 17, 2016. https://link.springer.com/chapter/10.1007/978-3-319-46487-9-6
- Huang, Y. et al. (2020), CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition, 2020 IEEE/CVF Conference on Computer Vision andPattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 5900-5909, doi: 10.1109/CVPR42600.2020.00594.
- Kulkarni, O. S., Deokar, S. M., Chaudhari, A. K., Patankar, S. S. and Kulkarni, J. V. (2017), Real Time Face Recognition Using LBP Features, 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017, pp. 1-5, doi: 10.1109/ICCUBEA.2017.8463886.
- Rani, G. E., Suresh, S. M, M. P., Abhiram, M., Surya, K. J. and Kumar, B. Y. A. N. (2022), Face Recognition Using Principal Component Analysis, 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 932-936, doi: 10.1109/ICACITE53722.2022.9823434.
- Wang, C., Fang, H., Zhong, Y., and Deng, W. (2022), MLFW: A Database for Face Recognition on Masked Faces, In: et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. 2022. doi: 10.1007/978-3-031-20233-9-18.
- Wang, Q., Zhang, P., Xiong, H., and Zhao, J. (2021), Face.evoLVe: A High-Performance Face Recognition Library, arXiv.org, Jul. 19, 2021. https://arxiv.org/abs/2107.08621v4
- Yi, D., Lei, Z., Liao, S., and Li, S. Z. (2014), Learning Face Representation from Scratch, arXiv.org, Nov. 28, 2014. https://arxiv.org/abs/1411.7923v1
- Zheng, T., Deng, W., and Hu, J. (2017), Cross-age LFW: A database for studying cross-age face recognition in unconstrained environments, CoRR, vol. abs/1708.08197, 2017.
- Zhong, Y., Deng, W., Hu, J., Zhao, D., Li, X. and Wen, D. (2021), SFace: Sigmoid- Constrained Hypersphere Loss for Robust Face Recognition, in IEEE Transactions on Image Processing, vol. 30, pp. 2587-2598, 2021, doi: 10.1109/TIP.2020.3048632.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Vision, Pattern Recognition, Semi- and Unsupervised Learning |
| Journal Section | Computer Engineering |
| Authors | |
| Publication Date | December 12, 2023 |
| Submission Date | August 9, 2023 |
| Published in Issue | Year 2023 |