A Survey of Face Recognition based on Convolutional Neural Network

Authors

  • Raymond Erz Saragih Universitas Universal
  • Quynh Huong To Yuan Ze University

DOI:

https://doi.org/10.24002/ijis.v4i2.5439

Keywords:

Convolutional Neural Network, Deep Learning, Face Recognition, Survey

Abstract

Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.

Author Biographies

Raymond Erz Saragih, Universitas Universal

Department of Informatics Engineering

Quynh Huong To, Yuan Ze University

Department of Industrial Engineering and Management

References

M. Chihaoui, A. Elkefi, W. Bellil, and C. Ben Amar, “A survey of 2D face recognition techniques,” Computers, vol. 5, no. 4, pp. 1–28, 2016.

U. Zafar et al., “Face recognition with Bayesian convolutional networks for robust surveillance systems,” EURASIP J. Image Video Process., vol. 2019, no. 1, p. 10, Dec. 2019.

Z. Lei and S. Z. Li, “Face Recognition Models: Computational Approaches,” in International Encyclopedia of the Social & Behavioral Sciences, vol. 8, Elsevier, 2015, pp. 658–662.

G. Hu, X. Peng, Y. Yang, T. M. Hospedales, and J. Verbeek, “Frankenstein: Learning Deep Face Representations Using Small Data,” IEEE Trans. Image Process., vol. 27, no. 1, pp. 293–303, 2018.

M. Peng, C. Wang, T. Chen, and G. Liu, “NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification,” Information, vol. 7, no. 4, p. 61, Oct. 2016.

S. Pouyanfar et al., “A Survey on Deep Learning,” ACM Comput. Surv., vol. 51, no. 5, pp. 1–36, 2018.

N. Aloysius and M. Geetha, “A review on deep convolutional neural networks,” Proc. 2017 IEEE Int. Conf. Commun. Signal Process. ICCSP 2017, vol. 2018-Janua, pp. 588–592, 2018.

Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016.

R. Ranjan et al., “Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans,” IEEE Signal Process. Mag., vol. 35, no. 1, pp. 66–83, Jan. 2018.

S. Almabdy and L. Elrefaei, “Deep Convolutional Neural Network-Based Approaches for Face Recognition,” Appl. Sci., vol. 9, no. 20, p. 4397, Oct. 2019.

S. Zhou and S. Xiao, “3D face recognition: a survey,” Human-centric Comput. Inf. Sci., vol. 8, no. 1, 2018.

S. B. Ahmed, S. F. Ali, J. Ahmad, M. Adnan, and M. M. Fraz, “On the frontiers of pose invariant face recognition: a review,” Artif. Intell. Rev., Jul. 2019.

P. Kaur, K. Krishan, S. K. Sharma, and T. Kanchan, “Facial-recognition algorithms: A literature review,” Med. Sci. Law, p. 002580241989316, Jan. 2020.

Q. Hua, C. Dong, and F. Zhang, “A Novel Approach to Face Verification Based on Second-Order Face-Pair Representation,” Complexity, vol. 2018, pp. 1–10, Jun. 2018.

A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review,” Artif. Intell. Rev., vol. 52, no. 2, pp. 927–948, 2019.

R. Ranjan et al., “A Fast and Accurate System for Face Detection, Identification, and Verification,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 1, no. 2, pp. 82–96, Apr. 2019.

D. Triantafyllidou, P. Nousi, and A. Tefas, “Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining,” Big Data Res., vol. 11, pp. 65–76, Mar. 2018.

S. Zafeiriou, C. Zhang, and Z. Zhang, “A survey on face detection in the wild: Past, present and future,” Comput. Vis. Image Underst., vol. 138, no. March, pp. 1–24, Sep. 2015.

D. Luo, G. Wen, D. Li, Y. Hu, and E. Huan, “Deep-learning-based face detection using iterative bounding-box regression,” Multimed. Tools Appl., vol. 77, no. 19, pp. 24663–24680, 2018.

W. Wu, Y. Yin, X. Wang, and D. Xu, “Face detection with different scales based on faster R-CNN,” IEEE Trans. Cybern., vol. 49, no. 11, pp. 4017–4028, 2019.

C. Peng, W. Bu, J. Xiao, K. Wong, and M. Yang, “An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance,” Appl. Sci., vol. 8, no. 11, p. 2222, Nov. 2018.

H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua, “A convolutional neural network cascade for face detection,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, vol. 07-12-June, pp. 5325–5334.

H. Qin, J. Yan, X. Li, and X. Hu, “Joint Training of Cascaded CNN for Face Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, vol. 2016-Decem, pp. 3456–3465.

D. D. Sawat and R. S. Hegadi, “Unconstrained face detection: a Deep learning and Machine learning combined approach,” CSI Trans. ICT, vol. 5, no. 2, pp. 195–199, Jun. 2017.

L. Zhang and X. Zhi, “A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection,” Futur. Internet, vol. 10, no. 8, p. 80, Aug. 2018.

X. Jin and X. Tan, “Face alignment in-the-wild: A Survey,” Comput. Vis. Image Underst., vol. 162, pp. 1–22, Sep. 2017.

Y. Wu and Q. Ji, “Facial Landmark Detection: A Literature Survey,” Int. J. Comput. Vis., vol. 127, no. 2, pp. 115–142, 2019.

H. Wang, J. Hu, and W. Deng, “Face Feature Extraction: A Complete Review,” IEEE Access, vol. 6, no. c, pp. 6001–6039, 2018.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors (Switzerland), vol. 20, no. 2, 2020.

H. Ben Fredj, S. Bouguezzi, and C. Souani, “Face recognition in unconstrained environment with CNN,” Vis. Comput., no. 0123456789, Jan. 2020.

Z. Pei, H. Xu, Y. Zhang, M. Guo, and Y.-H. Yang, “Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments,” Electronics, vol. 8, no. 10, p. 1088, Sep. 2019.

H.-M. Moon, C. H. Seo, and S. B. Pan, “A face recognition system based on convolution neural network using multiple distance face,” Soft Comput., vol. 21, no. 17, pp. 4995–5002, Sep. 2017.

Z. Jingxiao, J. Chen, N. Bodla, V. M. Patel, and R. Chellappa, “VLAD encoded Deep Convolutional features for unconstrained face verification,” in 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 4101–4106.

W. Hu, H. Hu, and X. Lu, “Heterogeneous Face Recognition Based on Multiple Deep Networks with Scatter Loss and Diversity Combination,” IEEE Access, vol. 7, pp. 75305–75317, 2019.

N. A. Binti Mat Kasim, N. H. Binti Abd Rahman, Z. Ibrahim, and N. N. Abu Mangshor, “Celebrity Face Recognition using Deep Learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 2, p. 476, Nov. 2018.

R. I. Bendjillali, M. Beladgham, K. Merit, and A. Taleb-Ahmed, “Illumination-robust face recognition based on deep convolutional neural networks architectures,” Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 2, p. 1015, May 2020.

G. P. Nam, H. Choi, J. Cho, and I. J. Kim, “PSI-CNN: A Pyramid-based scale-invariant cnn architecture for face recognition robust to various image resolutions,” Appl. Sci., vol. 8, no. 9, 2018.

P. S. Chandran, N. B. Byju, R. U. Deepak, K. N. Nishakumari, P. Devanand, and P. M. Sasi, “Missing child identification system using deep learning and multiclass SVM,” 2018 IEEE Recent Adv. Intell. Comput. Syst. RAICS 2018, pp. 113–116, 2019.

M. Z. Khan, S. Harous, S. U. Hassan, M. U. Ghani Khan, R. Iqbal, and S. Mumtaz, “Deep Unified Model for Face Recognition Based on Convolution Neural Network and Edge Computing,” IEEE Access, vol. 7, pp. 72622–72633, 2019.

C. Ding and D. Tao, “Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 1002–1014, 2018.

E. Zangeneh, M. Rahmati, and Y. Mohsenzadeh, “Low resolution face recognition using a two-branch deep convolutional neural network architecture,” Expert Syst. Appl., vol. 139, p. 112854, 2020.

Y. X. Yang, C. Wen, K. Xie, F. Q. Wen, G. Q. Sheng, and X. G. Tang, “Face recognition using the SR-CNN model,” Sensors (Switzerland), vol. 18, no. 12, 2018.

R. Singh and H. Om, “Newborn face recognition using deep convolutional neural network,” Multimed. Tools Appl., vol. 76, no. 18, pp. 19005–19015, 2017.

B. Ríos-Sánchez, D. Costa-da-Silva, N. Martín-Yuste, and C. Sánchez-Ávila, “Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions,” Appl. Sci., vol. 9, no. 24, p. 5474, Dec. 2019.

S. Zhou, C. Chen, G. Han, and X. Hou, “Double Additive Margin Softmax Loss for Face Recognition,” Appl. Sci., vol. 10, no. 1, p. 60, Dec. 2019.

S. Liu, Y. Song, M. Zhang, J. Zhao, S. Yang, and K. Hou, “An Identity Authentication Method Combining Liveness Detection and Face Recognition,” Sensors, vol. 19, no. 21, p. 4733, Oct. 2019.

N. T. Son et al., “Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College,” Symmetry (Basel)., vol. 12, no. 2, p. 307, Feb. 2020.

P. Li, J. Xie, W. Yan, Z. Li, and G. Kuang, “Living Face Verification via Multi-CNNs,” Int. J. Comput. Intell. Syst., vol. 12, no. 1, p. 183, 2018.

A. P. Song, Q. Hu, X. H. Ding, X. Y. Di, and Z. H. Song, “Similar Face Recognition Using the IE-CNN Model,” IEEE Access, vol. 8, pp. 45244–45253, 2020.

Z. Ma, Y. Ding, B. Li, and X. Yuan, “Deep CNNs with robust LBP guiding pooling for face recognition,” Sensors (Switzerland), vol. 18, no. 11, pp. 1–18, 2018.

M. Nimbarte and K. Bhoyar, “Age Invariant Face Recognition using Convolutional Neural Network,” Int. J. Electr. Comput. Eng., vol. 8, no. 4, p. 2126, Aug. 2018.

P. Kamencay, M. Benco, T. Mizdos, and R. Radil, “A new method for face recognition using convolutional neural network,” Adv. Electr. Electron. Eng., vol. 15, no. 4 Special Issue, pp. 663–672, 2017.

M. O. Simón et al., “Improved RGB-D-T based face recognition,” IET Biometrics, vol. 5, no. 4, pp. 297–303, Dec. 2016.

J. Zeng, X. Zhao, J. Gan, C. Mai, Y. Zhai, and F. Wang, “Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition,” Comput. Intell. Neurosci., vol. 2018, pp. 1–11, Aug. 2018.

J.-C. Chen et al., “Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks,” Int. J. Comput. Vis., vol. 126, no. 2–4, pp. 272–291, Apr. 2018.

J.-C. Chen, V. M. Patel, and R. Chellappa, “Unconstrained face verification using deep CNN features,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1–9.

G. Chen, Y. Shao, C. Tang, Z. Jin, and J. Zhang, “Deep transformation learning for face recognition in the unconstrained scene,” Mach. Vis. Appl., vol. 29, no. 3, pp. 513–523, Apr. 2018.

H. Ling, J. Wu, J. Huang, J. Chen, and P. Li, “Attention-based convolutional neural network for deep face recognition,” Multimed. Tools Appl., vol. 79, no. 9–10, pp. 5595–5616, Mar. 2020.

S. Khan, A. Akram, and N. Usman, “Real Time Automatic Attendance System for Face Recognition Using Face API and OpenCV,” Wirel. Pers. Commun., vol. 113, no. 1, pp. 469–480, 2020.

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: A Dataset for Recognising Faces across Pose and Age,” in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 67–74.

Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9907 LNCS, 2016, pp. 87–102.

H.-W. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 343–347.

S. S. Gangonda, P. P. Patavardhan, and K. J. Karande, “A Comprehensive Survey of Face Databases for Constrained and Unconstrained Environments,” Proc. - 2018 IEEE Glob. Conf. Wirel. Comput. Networking, GCWCN 2018, pp. 173–177, 2019.

D. Yi, Z. Lei, S. Liao, and S. Z. Li, “Learning Face Representation from Scratch,” Nov. 2014.

R. Min, N. Kose, and J.-L. Dugelay, “KinectFaceDB: A Kinect Database for Face Recognition,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 44, no. 11, pp. 1534–1548, Nov. 2014.

B. C. Chen, C. S. Chen, and W. H. Hsu, “Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset,” IEEE Trans. Multimed., vol. 17, no. 6, pp. 804–815, 2015.

Downloads

Published

2022-02-27

How to Cite

Saragih, R. E., & To, Q. H. (2022). A Survey of Face Recognition based on Convolutional Neural Network. Indonesian Journal of Information Systems, 4(2). https://doi.org/10.24002/ijis.v4i2.5439