Applied computing Journal

Volume 2 , Issue 2 , April 2022

Face Recognition based on Convoluted Neural Networks: Technical Review

  • Basil Ismail Mirghani Shakkak ,
  • SARA ALI K. M. AL MAZRUII
Basil Ismail Mirghani Shakkak
Sohar University FCIT
SARA ALI K. M. AL MAZRUII
Sohar University, FCIT , Sohar, Oman.

Abstract

Human beings recognize and classify objects with biological senses and brain that processes the input into meaningful information. Other than that humans have come to recognize each other in multiple ways one of which is visual recognition of faces. As a biological trait human faces are certainly a biometric such they are universal, distinctive, mostly permanent and collectable. With that a computerized face recognition system can constructed relying on visual information present on each face uniquely. Generally a face recognition system consists of two main phases, face detection phase where presence of a human face is verified on visual input and face recognition phase where detected face is processed for identification. One of the most sought after methods in field image processing for face recognition is CNN (Convoluted Neural Networks). CNNs have proved its effectiveness and accuracy in many CNN based face detection and face recognition systems. As such in this paper the architecture of CNN is presented. Then different techniques for face detection and face recognition based on CNNs are reviewed. In reviewed papers CNNs have repeatedly demonstrated effectiveness and accuracy on multiple benchmarks for face recognition application.

PDF

References

  1. • Jain, A., Ross, A., & Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20. https://doi.org/10.1109/tcsvt.2003.818349
  2. • Li, L., Mu, X., Li, S., & Peng, H. (2020). A Review of Face Recognition Technology. IEEE Access, 8, 139110–139120. https://doi.org/10.1109/access.2020.3011028
  3. • Oloyede, M. O., Hancke, G. P., & Myburgh, H. C. (2020). A review on face recognition systems: recent approaches and challenges. Multimedia Tools and Applications, 79(37–38), 27891–27922. https://doi.org/10.1007/s11042-020-09261-2
  4. • Oloyede, M. O., & Hancke, G. P. (2016). Unimodal and Multimodal Biometric Sensing Systems: A Review. IEEE Access, 4, 7532–7555. https://doi.org/10.1109/access.2016.2614720
  5. • Jin, X., & Tan, X. (2017). Face alignment in-the-wild: A Survey. Computer Vision and Image Understanding, 162, 1–22. https://doi.org/10.1016/j.cviu.2017.08.008
  6. • Karamizadeh, S., Abdullah, S. M., Zamani, M., Shayan, J., & Nooralishahi, P. (2016). Face Recognition via Taxonomy of Illumination Normalization. Intelligent Systems Reference Library, 139–160. https://doi.org/10.1007/978-3-319-44270-9_7
  7. • Coventry, L., de Angeli, A., & Johnson, G. (2003). Honest it’s me! Self service verification. Paper Presented at Workshop on Human-Computer Interaction and Security Systems, Fort Lauderdale, Florida, United States, 1–4. https://www.andrewpatrick.ca/CHI2003/HCISEC/HCISEC-papers.html
  8. • Ganorkar, S. R., & Ghatol, A. A. (2007). Iris Recognition: An Emerging Biometric Technology. Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece. Published.
  9. • P Tripathi, K. (2011). A Comparative Study of Biometric Technologies with Reference to Human Interface. International Journal of Computer Applications, 14(5), 10–15. https://doi.org/10.5120/1842-2493
  10. • Muhtahir, O. O., Adeyinka, A. O., & Kayode, A. S. (2013). Fingerprint Biometric Authentication for Enhancing Staff Attendance System. International Journal of Applied Information Systems, 5(3).
  11. • Ahmad, S. M. S., Ali, B. M., & Adnan, W. A. W. (2012). Technical Issues and Challenges of Biometric Applications as Access Control Tools of Information Security. International Journal of Innovative Computing, Information and Control, 8(11), 7983–7999.
  12. • S. Manjula, V., & S. Santhosh Baboo, L. D. (2012). Face Detection Identification and Tracking by PRDIT Algorithm using Image Database for Crime Investigation. International Journal of Computer Applications, 38(10), 40–46. https://doi.org/10.5120/4741-6649
  13. • Lander, K., Bruce, V., & Bindemann, M. (2018). Use-inspired basic research on individual differences in face identification: implications for criminal investigation and security. Cognitive Research: Principles and Implications, 3(1). https://doi.org/10.1186/s41235-018-0115-6
  14. • Hu, Y., An, H., Guo, Y., Zhang, C., Zhang, T., & Ye, L. (2010). The Development Status and Prospects on the Face Recognition. 2010 4th International Conference on Bioinformatics and Biomedical Engineering. Published. https://doi.org/10.1109/icbbe.2010.5517197
  15. • Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
  16. • Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Published. https://doi.org/10.1109/cvpr.2014.220
  17. • Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/tpami.2016.2577031
  18. • Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013). Learning Hierarchical Features for Scene Labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915–1929. https://doi.org/10.1109/tpami.2012.231
  19. • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  20. • Bezdan, T., & Bačanin Džakula, N. (2019). Convolutional Neural Network Layers and Architectures. Proceedings of the International Scientific Conference - Sinteza 2019. Published. https://doi.org/10.15308/sinteza-2019-445-451
  21. • Salomon, M., Couturier, R., Guyeux, C., Couchot, J. F., & Bahi, J. (2017). Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key: A deep learning approach for telemedicine. European Research in Telemedicine / La Recherche Européenne En Télémédecine, 6(2), 79–92. https://doi.org/10.1016/j.eurtel.2017.06.001
  22. • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
  23. • Ignjatić, J., Nikolić, B., Rikalović, A., & ĆUlibrk, D. (2018). Deep Learning for Historical Cadastral Maps Digitization: Overview, Challenges and Potential. WSCG 2018 - Poster Papers Proceedings. Published. https://doi.org/10.24132/csrn.2018.2803.6
  24. • Triantafyllidou, D., & Tefas, A. (2016). Face detection based on deep convolutional neural networks exploiting incremental facial part learning. 2016 23rd International Conference on Pattern Recognition (ICPR). Published. https://doi.org/10.1109/icpr.2016.7900186
  25. • Farfade, S. S., Saberian, M. J., & Li, L. J. (2015). Multi-view Face Detection Using Deep Convolutional Neural Networks. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. Published. https://doi.org/10.1145/2671188.2749408
  26. • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Published. https://doi.org/10.1109/cvpr.2014.81
  27. • Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645. https://doi.org/10.1109/tpami.2009.167
  28. • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2016.91
  29. • Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2015.7299170
  30. • Yang, S., Luo, P., Loy, C. C., & Tang, X. (2018). Faceness-Net: Face Detection through Deep Facial Part Responses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8), 1845–1859. https://doi.org/10.1109/tpami.2017.2738644
  31. • Qin, H., Yan, J., Li, X., & Hu, X. (2016). Joint Training of Cascaded CNN for Face Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2016.376
  32. • Garg, D., Goel, P., Pandya, S., Ganatra, A., & Kotecha, K. (2018). A Deep Learning Approach for Face Detection using YOLO. 2018 IEEE Punecon. Published. https://doi.org/10.1109/punecon.2018.8745376
  33. • Liu, W., Zhou, L., & Chen, J. (2021). Face Recognition Based on Lightweight Convolutional Neural Networks. Information, 12(5), 191. https://doi.org/10.3390/info12050191
  34. • Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Published. https://doi.org/10.1109/cvpr.2018.00745
  35. • Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2019.00482
  36. • Nimbarte, M., & Bhoyar, K. (2018). Age Invariant Face Recognition using Convolutional Neural Network. International Journal of Electrical and Computer Engineering (IJECE), 8(4), 2126. https://doi.org/10.11591/ijece.v8i4.pp2126-2138
  37. • Tang, J., Su, Q., Su, B., Fong, S., Cao, W., & Gong, X. (2020). Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition. Computer Methods and Programs in Biomedicine, 197, 105622. https://doi.org/10.1016/j.cmpb.2020.105622
  38. • Khalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2013). Face Recognition Using Convolutional Neural Network and Simple Logistic Classifier. Advances in Intelligent Systems and Computing, 197–207. https://doi.org/10.1007/978-3-319-00930-8_18
  39. • Ramaiah, N. P., Ijjina, E. P., & Mohan, C. K. (2015). Illumination invariant face recognition using convolutional neural networks. 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). Published. https://doi.org/10.1109/spices.2015.7091490
  40. • Nakada, M., Wang, H., & Terzopoulos, D. (2017). AcFR: Active Face Recognition Using Convolutional Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Published. https://doi.org/10.1109/cvprw.2017.11
  41. • Mathias, M., Benenson, R., Pedersoli, M., & van Gool, L. (2014). Face Detection without Bells and Whistles. Computer Vision – ECCV 2014, 720–735. https://doi.org/10.1007/978-3-319-10593-2_47
  42. • Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2015.7298682
  43. • Sun, Y., Wang, X., & Tang, X. (2015). Deeply learned face representations are sparse, selective, and robust. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published. https://doi.org/10.1109/cvpr.2015.7298907
  44. • Sanchez-Moreno, A. S., Olivares-Mercado, J., Hernandez-Suarez, A., Toscano-Medina, K., Sanchez-Perez, G., & Benitez-Garcia, G. (2021). Efficient Face Recognition System for Operating in Unconstrained Environments. Journal of Imaging, 7(9), 161. https://doi.org/10.3390/jimaging7090161
  45. • William, I., Ignatius Moses Setiadi, D. R., Rachmawanto, E. H., Santoso, H. A., & Sari, C. A. (2019). Face Recognition using FaceNet (Survey, Performance Test, and Comparison). 2019 Fourth International Conference on Informatics and Computing (ICIC). Published. https://doi.org/10.1109/icic47613.2019.8985786
  46. • Khan, S., Javed, M. H., Ahmed, E., Shah, S. A. A., & Ali, S. U. (2019). Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses. 2019 International Conference on Information Science and Communication Technology (ICISCT). Published. https://doi.org/10.1109/cisct.2019.8777442
DOI: https://doi.org/10.52098/acj.202247
Published April 16, 2022
How to Cite
Shakkak, B. I. M., & SARA ALI K. M. AL MAZRUII. (2022). Face Recognition based on Convoluted Neural Networks: Technical Review. Applied Computing Journal, 2(2), 193-212. https://doi.org/10.52098/acj.202247

Copyright (c) 2022 Copyright (c) 2022 Copyright (c) 2022 Author(s) of this work retain copyright and grant the journal right, publishing license of first publication with the work simultaneously licensed under Creative Commons Attribution International License (CC BY 4.0). Author(s) and ACAA permit unrestricted use, distribution, and reproduction in any medium, provided the original work with proper citation. ACAA publishing house is located in Oman / Sohar , Zip code 311. ISNI 0000 0004 9361 5767

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.