Gender estimation based on deep learned and handcrafted features in an uncontrolled environment
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s00530-022-01011-8.pdf
Reference52 articles.
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2. Amri, R., Gazdar, A., Barhoumi, W.: A comparative study on the importance of each face part in facial gender recognition via convolutional neural networks. In: 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA), pp 1–86, IEEE (2021)
3. Aslam, A., Hussain, B., Cetin, A.E., Umar, A.I., Ansari, R.: Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network. J. Electron. Imaging 27(5), 053–023 (2018)
4. Aslam, A., Hayat, K., Umar, A.I., Zohuri, B., Zarkesh-Ha, P., Modissette, D., Khan, S.Z., Hussian, B.: Wavelet-based convolutional neural networks for gender classification. J. Electron. Imaging 28(1), 013012 (2019)
5. Chen, W.S., Jeng, R.H.: A new patch-based lbp with adaptive weights for gender classification of human face. J. Chin. Inst. Eng. 1–7 (2020)
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