A Review: Face Recognition Techniques using Deep Learning
DOI:
https://doi.org/10.33193/IJSER.1.1.2022.33Keywords:
Face recognition, Deep Learning, CNN -Feature extractionAbstract
Face recognition (FR) is one of the most significant types of research that are widely used in various areas, such as finance, preventing crime, protecting the border, and for military purposes. Face recognition is a biometric identification technology based on human facial feature information. There are two main approaches first one is hand-crafted (HC) features which is the traditional method (geometry-based, holistic, feature based, and hybrid methods), and the recent one is based on deep learning (DL). The major purpose of this work to provide UpToDate literature review for face recognition (FR) Techniques. Furthermore, it summarizes the benchmark datasets and the most successful methods used on these datasets for face recognition.
References
[1] M. T. H. Fuad et al., “Recent advances in deep learning techniques for face recognition,” IEEE Access, vol. 9, pp. 99112–99142, 2021, doi: 10.1109/ACCESS.2021.3096136.
[2] A. Mutrak, “Intelligent Virtual Assistant - VISION,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 5, pp. 2057–2060, 2021, doi: 10.22214/ijraset.2021.34757.
[3] D. Silver et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” pp. 1–19, 2017, [Online]. Available: http://arxiv.org/abs/1712.01815
[4] C. Vision, D. Algorithm, F. Expression, and H. Liu, “Face Technologies on Mobile Devices Face Detection and Face Direction Esti- mation Using Color and Shape Features,” 2015.
[5] Institute of Electrical and Electronics Engineers. and IEEE Photonics Society., “2011 Symposium on Photonics and Optoelectronics (SOPO) : May 16-18, 2011, Wuhan, China,” no. 2, pp. 3–6, 2011.
[6] N. Samadiani et al., “A review on automatic facial expression recognition systems assisted by multimodal sensor data,” Sensors (Switzerland), vol. 19, no. 8, pp. 1–27, 2019, doi: 10.3390/s19081863.
[7] S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, “Custom face classification model for classroom using haar-like and lbp features with their performance comparisons,” Electron., vol. 10, no. 2, pp. 1–15, 2021, doi: 10.3390/electronics10020102.
[8] I. Technology, “A REVIEW ON FACIAL RECOGNITION INCLUDING LOCAL, HOLISTIC AND HYBRID APPROACHES Prince Goyal, Heena wadhwa,” vol. 21, no. 2, pp. 210–216, 2020.
[1] M. T. H. Fuad et al., “Recent advances in deep learning techniques for face recognition,” IEEE Access, vol. 9, pp. 99112–99142, 2021, doi: 10.1109/ACCESS.2021.3096136.
[2] A. Mutrak, “Intelligent Virtual Assistant - VISION,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 5, pp. 2057–2060, 2021, doi: 10.22214/ijraset.2021.34757.
[3] D. Silver et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” pp. 1–19, 2017, [Online]. Available: http://arxiv.org/abs/1712.01815
[4] C. Vision, D. Algorithm, F. Expression, and H. Liu, “Face Technologies on Mobile Devices Face Detection and Face Direction Esti- mation Using Color and Shape Features,” 2015.
[5] Institute of Electrical and Electronics Engineers. and IEEE Photonics Society., “2011 Symposium on Photonics and Optoelectronics (SOPO) : May 16-18, 2011, Wuhan, China,” no. 2, pp. 3–6, 2011.
[6] N. Samadiani et al., “A review on automatic facial expression recognition systems assisted by multimodal sensor data,” Sensors (Switzerland), vol. 19, no. 8, pp. 1–27, 2019, doi: 10.3390/s19081863.
[7] S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, “Custom face classification model for classroom using haar-like and lbp features with their performance comparisons,” Electron., vol. 10, no. 2, pp. 1–15, 2021, doi: 10.3390/electronics10020102.
[8] I. Technology, “A REVIEW ON FACIAL RECOGNITION INCLUDING LOCAL, HOLISTIC AND HYBRID APPROACHES Prince Goyal, Heena wadhwa,” vol. 21, no. 2, pp. 210–216, 2020.
[9] S. Dhawan and N. Khurana, “Volume 2 , Issue 2 ( February 2012 ) A REVIEW OF FACE RECOGNITION ISSN : 2249-3905 IJREAS Volume 2 , Issue 2 ( February 2012 ) ISSN : 2249-3905,” vol. 2, no. 2, pp. 835–846, 2012.
[10] A. A. Fathima, S. Ajitha, V. Vaidehi, M. Hemalatha, R. Karthigaiveni, and R. Kumar, “Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis,” 2015 IEEE Int. Conf. Comput. Graph. Vis. Inf. Secur. CGVIS 2015, pp. 220–225, 2016, doi: 10.1109/CGVIS.2015.7449925.
[11] D. Graupe, “Deep Learning Convolutional Neural Network,” Deep Learn. Neural Networks, pp. 41–55, 2016, doi: 10.1142/9789813146464_0005.
[12] S. Almabdy and L. Elrefaei, “Deep convolutional neural network-based approaches for face recognition,” Appl. Sci., vol. 9, no. 20, 2019, doi: 10.3390/app9204397.
[13] K. B. Pranav and J. Manikandan, “Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 1651–1659, 2020, doi: 10.1016/j.procs.2020.04.177.
[14] W. H. Lopez Pinaya, S. Vieira, R. Garcia-Dias, and A. Mechelli, “Autoencoders,” Mach. Learn. Methods Appl. to Brain Disord., pp. 193–208, 2019, doi: 10.1016/B978-0-12-815739-8.00011-0.
[15] H. Sewani and R. Kashef, “An autoencoder-based deep learning classifier for efficient diagnosis of autism,” Children, vol. 7, no. 10, 2020, doi: 10.3390/children7100182.
[16] A. Aggarwal, M. Mittal, and G. Battineni, “Generative adversarial network: An overview of theory and applications,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100004, 2021, doi: 10.1016/j.jjimei.2020.100004.
[17] Z. B. Lahaw, D. Essaidani, and H. Seddik, “Robust Face Recognition Approaches Using PCA , ICA , LDA Based on DWT , and SVM algorithms,” 2018 41st Int. Conf. Telecommun. Signal Process., no. January 2022, pp. 1–5, 2018, doi: 10.1109/TSP.2018.8441452.
[18] S. Zhang, Q. Miao, X. Zhu, Y. Chen, Z. Lei, and J. Wang, “POSE-WEIGHTED GAN FOR PHOTOREALISTIC FACE FRONTALIZATION University of Chinese Academy of Sciences 2 National Laboratory of Pattern Recognition , Institute of Automation Chinese Academy of Sciences , Beijing , China , 100190,” 2019 IEEE Int. Conf. Image Process., pp. 2384–2388, 2019.
[19] L. Du and H. Hu, “Neurocomputing Nuclear norm based adapted occlusion dictionary learning for face recognition with occlusion and illumination changes,” Neurocomputing, vol. 340, pp. 133–144, 2019, doi: 10.1016/j.neucom.2019.02.053.
[20] Q. Wang, T. Wu, H. Zheng, and G. Guo, “Hierarchical pyramid diverse attention networks for face recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 8323–8332. doi: 10.1109/CVPR42600.2020.00835.
[21] D. Metric et al., “PT,” 2017, doi: 10.1016/j.patcog.2017.10.015.
[22] L. Patil and V. D. Mytri, “Face recognition with CNN and inception deep learning models,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 1932–1938, 2019, doi: 10.35940/ijrte.C4476.098319.
[23] M. N. Elbedwehy and G. M. Behery, “Face Recognition Based on Relative Gradient Magnitude Strength,” Arab. J. Sci. Eng., 2020, doi: 10.1007/s13369-020-04538-y.
[24] N. Zhang and W. Deng, “Fine-grained LFW database,” 2016 Int. Conf. Biometrics, ICB 2016, pp. 1–11, 2016, doi: 10.1109/ICB.2016.7550057.
[25] C. Engineering and S. Domain, “Deep Learning based Human Recognition using Integration of GAN and Spatial Domain Techniques,” vol. 21, no. 8, 2021.
[2] A. Mutrak, “Intelligent Virtual Assistant - VISION,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 5, pp. 2057–2060, 2021, doi: 10.22214/ijraset.2021.34757.
[3] D. Silver et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” pp. 1–19, 2017, [Online]. Available: http://arxiv.org/abs/1712.01815
[4] C. Vision, D. Algorithm, F. Expression, and H. Liu, “Face Technologies on Mobile Devices Face Detection and Face Direction Esti- mation Using Color and Shape Features,” 2015.
[5] Institute of Electrical and Electronics Engineers. and IEEE Photonics Society., “2011 Symposium on Photonics and Optoelectronics (SOPO) : May 16-18, 2011, Wuhan, China,” no. 2, pp. 3–6, 2011.
[6] N. Samadiani et al., “A review on automatic facial expression recognition systems assisted by multimodal sensor data,” Sensors (Switzerland), vol. 19, no. 8, pp. 1–27, 2019, doi: 10.3390/s19081863.
[7] S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, “Custom face classification model for classroom using haar-like and lbp features with their performance comparisons,” Electron., vol. 10, no. 2, pp. 1–15, 2021, doi: 10.3390/electronics10020102.
[8] I. Technology, “A REVIEW ON FACIAL RECOGNITION INCLUDING LOCAL, HOLISTIC AND HYBRID APPROACHES Prince Goyal, Heena wadhwa,” vol. 21, no. 2, pp. 210–216, 2020.
[1] M. T. H. Fuad et al., “Recent advances in deep learning techniques for face recognition,” IEEE Access, vol. 9, pp. 99112–99142, 2021, doi: 10.1109/ACCESS.2021.3096136.
[2] A. Mutrak, “Intelligent Virtual Assistant - VISION,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 5, pp. 2057–2060, 2021, doi: 10.22214/ijraset.2021.34757.
[3] D. Silver et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,” pp. 1–19, 2017, [Online]. Available: http://arxiv.org/abs/1712.01815
[4] C. Vision, D. Algorithm, F. Expression, and H. Liu, “Face Technologies on Mobile Devices Face Detection and Face Direction Esti- mation Using Color and Shape Features,” 2015.
[5] Institute of Electrical and Electronics Engineers. and IEEE Photonics Society., “2011 Symposium on Photonics and Optoelectronics (SOPO) : May 16-18, 2011, Wuhan, China,” no. 2, pp. 3–6, 2011.
[6] N. Samadiani et al., “A review on automatic facial expression recognition systems assisted by multimodal sensor data,” Sensors (Switzerland), vol. 19, no. 8, pp. 1–27, 2019, doi: 10.3390/s19081863.
[7] S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, “Custom face classification model for classroom using haar-like and lbp features with their performance comparisons,” Electron., vol. 10, no. 2, pp. 1–15, 2021, doi: 10.3390/electronics10020102.
[8] I. Technology, “A REVIEW ON FACIAL RECOGNITION INCLUDING LOCAL, HOLISTIC AND HYBRID APPROACHES Prince Goyal, Heena wadhwa,” vol. 21, no. 2, pp. 210–216, 2020.
[9] S. Dhawan and N. Khurana, “Volume 2 , Issue 2 ( February 2012 ) A REVIEW OF FACE RECOGNITION ISSN : 2249-3905 IJREAS Volume 2 , Issue 2 ( February 2012 ) ISSN : 2249-3905,” vol. 2, no. 2, pp. 835–846, 2012.
[10] A. A. Fathima, S. Ajitha, V. Vaidehi, M. Hemalatha, R. Karthigaiveni, and R. Kumar, “Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis,” 2015 IEEE Int. Conf. Comput. Graph. Vis. Inf. Secur. CGVIS 2015, pp. 220–225, 2016, doi: 10.1109/CGVIS.2015.7449925.
[11] D. Graupe, “Deep Learning Convolutional Neural Network,” Deep Learn. Neural Networks, pp. 41–55, 2016, doi: 10.1142/9789813146464_0005.
[12] S. Almabdy and L. Elrefaei, “Deep convolutional neural network-based approaches for face recognition,” Appl. Sci., vol. 9, no. 20, 2019, doi: 10.3390/app9204397.
[13] K. B. Pranav and J. Manikandan, “Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 1651–1659, 2020, doi: 10.1016/j.procs.2020.04.177.
[14] W. H. Lopez Pinaya, S. Vieira, R. Garcia-Dias, and A. Mechelli, “Autoencoders,” Mach. Learn. Methods Appl. to Brain Disord., pp. 193–208, 2019, doi: 10.1016/B978-0-12-815739-8.00011-0.
[15] H. Sewani and R. Kashef, “An autoencoder-based deep learning classifier for efficient diagnosis of autism,” Children, vol. 7, no. 10, 2020, doi: 10.3390/children7100182.
[16] A. Aggarwal, M. Mittal, and G. Battineni, “Generative adversarial network: An overview of theory and applications,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100004, 2021, doi: 10.1016/j.jjimei.2020.100004.
[17] Z. B. Lahaw, D. Essaidani, and H. Seddik, “Robust Face Recognition Approaches Using PCA , ICA , LDA Based on DWT , and SVM algorithms,” 2018 41st Int. Conf. Telecommun. Signal Process., no. January 2022, pp. 1–5, 2018, doi: 10.1109/TSP.2018.8441452.
[18] S. Zhang, Q. Miao, X. Zhu, Y. Chen, Z. Lei, and J. Wang, “POSE-WEIGHTED GAN FOR PHOTOREALISTIC FACE FRONTALIZATION University of Chinese Academy of Sciences 2 National Laboratory of Pattern Recognition , Institute of Automation Chinese Academy of Sciences , Beijing , China , 100190,” 2019 IEEE Int. Conf. Image Process., pp. 2384–2388, 2019.
[19] L. Du and H. Hu, “Neurocomputing Nuclear norm based adapted occlusion dictionary learning for face recognition with occlusion and illumination changes,” Neurocomputing, vol. 340, pp. 133–144, 2019, doi: 10.1016/j.neucom.2019.02.053.
[20] Q. Wang, T. Wu, H. Zheng, and G. Guo, “Hierarchical pyramid diverse attention networks for face recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 8323–8332. doi: 10.1109/CVPR42600.2020.00835.
[21] D. Metric et al., “PT,” 2017, doi: 10.1016/j.patcog.2017.10.015.
[22] L. Patil and V. D. Mytri, “Face recognition with CNN and inception deep learning models,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 1932–1938, 2019, doi: 10.35940/ijrte.C4476.098319.
[23] M. N. Elbedwehy and G. M. Behery, “Face Recognition Based on Relative Gradient Magnitude Strength,” Arab. J. Sci. Eng., 2020, doi: 10.1007/s13369-020-04538-y.
[24] N. Zhang and W. Deng, “Fine-grained LFW database,” 2016 Int. Conf. Biometrics, ICB 2016, pp. 1–11, 2016, doi: 10.1109/ICB.2016.7550057.
[25] C. Engineering and S. Domain, “Deep Learning based Human Recognition using Integration of GAN and Spatial Domain Techniques,” vol. 21, no. 8, 2021.
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Published
2022-09-17
How to Cite
Hummady, G. K., & Ahmad, A. P. M. L. (2022). A Review: Face Recognition Techniques using Deep Learning. Al-Iraqia Journal for Scientific Engineering Research, 1(1), 1–9. https://doi.org/10.33193/IJSER.1.1.2022.33
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