Face Recognition using Deep Learning and TensorFlow framework
Makrem Beldi
beldimakrem@gmail.comThe National Engineering School of Tunis or ENIT, Tunisia (Tunisia)
Abstract
Detecting human faces and recognizing faces and facial expressions have always been an area of interest for different applications such as games, utilities and even security. With the advancement of machine learning, the techniques of detection and recognition have become more accurate and precise than ever before. However, machine learning remains a relatively complex field that could feel intimidating or inaccessible to many of us. Luckily, in the last couple of years, several organizations and open-source communities have been developing tools and libraries that help abstract the complex mathematical algorithms in order to encourage developers to easily create learning models and train them using any programming languages.
As part of this project, we will create a Face Detection framework in Python built on top of the work of several open-source projects and models with the hope to reduce the entry barrier for developers and to encourage them to focus more on developing innovative applications that make use of face detection and recognition.
Keywords:
FaceRecognition, FaceDetection, TensorFlow, CNNReferences
R. Rajeev et al. A fast and accurate system for face detection, identification, and verification. IEEE Transactions on Biometrics, Behavior, and Identity Science 1(2) (2019) 82-96.
DOI: https://doi.org/10.1109/TBIOM.2019.2908436
Google Scholar
H. Wang, J. Hu, W. Deng, Face feature extraction: a complete review, IEEE Access 6 (2017) 6001-6039.
DOI: https://doi.org/10.1109/ACCESS.2017.2784842
Google Scholar
M. Sharif, M.Y Javed, Javed, M. Sajjad, Face recognition based on facial features. Research Journal of Applied Sciences, Engineering and Technology 4(17) (2012) 2879-2886.
Google Scholar
L. Shen, L. Bai, A review on Gabor wavelets for face recognition, Pattern analysis and applications 9 (2006) 273-292.
Google Scholar
S. Anwarul, D. Susheela, A comprehensive review on face recognition methods and factors affecting facial recognition accuracy, Proceedings of ICRIC 2019: Recent Innovations in Computing (2020) 495-514.
DOI: https://doi.org/10.1007/978-3-030-29407-6_36
Google Scholar
M. Owayjan, A. Dergham, G. Haber, N. Fakih, A. Hamoush, E. Abdo, New trends in networking, computing, E-learning, systems sciences, and engineering, Springer International Publishing, 2015.
Google Scholar
J.H. Li, Cyber security meets artificial intelligence: a survey, Frontiers of Information Technology & Electronic Engineering 19(12) (2018) 1462-1474.
DOI: https://doi.org/10.1631/FITEE.1800573
Google Scholar
B. Gupta, D.P. Agrawal, S. Yamaguchi, Handbook of research on modern cryptographic solutions for computer and cyber security, IGI global, 2016.
DOI: https://doi.org/10.4018/978-1-5225-0105-3
Google Scholar
P.M. Kumar, U. Gandhi, R. Varatharajan, G. Manogaran, T. Vadivel, Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things, Cluster Computing 22 (2019) 7733-7744.
DOI: https://doi.org/10.1007/s10586-017-1323-4
Google Scholar
M.S. Obaidat, I. Traore, I. Woungang, Biometric-based physical and cybersecurity systems. Cham: Springer International Publishing, 2019.
DOI: https://doi.org/10.1007/978-3-319-98734-7
Google Scholar
Z. Ma et al., Lightweight privacy-preserving ensemble classification for face recognition, IEEE Internet of Things Journal 6(3) (2019) 5778-5790.
DOI: https://doi.org/10.1109/JIOT.2019.2905555
Google Scholar
Z. Zhang et al., Artificial intelligence in cyber security: research advances, challenges, and opportunities, Artificial Intelligence Review (2022) 1-25.
Google Scholar
R. Chellappa, P. Sinha, P.J. Phillips, Face recognition by computers and humans, Computer 43(2) (2010) 46-55.
DOI: https://doi.org/10.1109/MC.2010.37
Google Scholar
M. Sahani et al., Web-based online embedded door access control and home security system based on face recognition, 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]. IEEE, 2015.
DOI: https://doi.org/10.1109/ICCPCT.2015.7159473
Google Scholar
M. Owayjan et al., Face recognition security system. New trends in networking, computing, E-learning, systems sciences, and engineering, Springer International Publishing, 2015.
DOI: https://doi.org/10.1007/978-3-319-06764-3_42
Google Scholar
S.A. Radzi et al., IoT based facial recognition door access control home security system using raspberry pi, International Journal of Power Electronics and Drive Systems 11(1) (2020) 417-424.
DOI: https://doi.org/10.11591/ijpeds.v11.i1.pp417-424
Google Scholar
S. Trivedi, P. Nikhil, Virtual Employee Monitoring: A Review on Tools, Opportunities, Challenges, and Decision Factors, Empirical Quests for Management Essences 1(1) (2021) 86-99.
Google Scholar
H. Ai, X, Cheng. Research on embedded access control security system and face recognition system, Measurement 123 (2018) 309-322.
DOI: https://doi.org/10.1016/j.measurement.2018.04.005
Google Scholar
L. Sirovich, M. Kirby, Low-dimensional procedure for the characterization of human faces, Josa a 4(3) (1987) 519-524.
DOI: https://doi.org/10.1364/JOSAA.4.000519
Google Scholar
A. K. Jain, R. C. Dubes, Algorithms for Clustering Data. New Jersey: Prentice-Hall, 1988.
Google Scholar
K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Boston, MA: Academic Press, 1990.
DOI: https://doi.org/10.1016/B978-0-08-047865-4.50007-7
Google Scholar
M. Ringnér, What is principal component analysis?. Nature biotechnology 26(3) (2008) 303-304.
DOI: https://doi.org/10.1038/nbt0308-303
Google Scholar
J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Face recognition using LDA-based algorithms, IEEE Transactions on Neural networks 14(1) (2003) 195-200.
DOI: https://doi.org/10.1109/TNN.2002.806647
Google Scholar
J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Regularized D-LDA for face recognition. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03).Vol. 3. IEEE, 2003.
Google Scholar
G.L. Marcialis, R. Fabio, Fusion of LDA and PCA for Face Recognition. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza díArmi (2002).
DOI: https://doi.org/10.1007/3-540-47917-1_4
Google Scholar
A. Bansal, M. Kapil, A. Sahil, Face recognition using PCA and LDA algorithm. 2012 second international conference on Advanced Computing & Communication Technologies. IEEE, 2012.
DOI: https://doi.org/10.1109/ACCT.2012.52
Google Scholar
T. Ojala, P. Matti, H. David, A comparative study of texture measures with classification based on featured distributions, Pattern recognition 29(1) (1996) 51-59.
DOI: https://doi.org/10.1016/0031-3203(95)00067-4
Google Scholar
T. Ahonen, M Pietikäinen, Soft histograms for local binary patterns, Proceedings of the Finnish signal processing symposium, FINSIG 5(9) 2007.
Google Scholar
R. Hadsell, C. Sumit, L. Yann, Dimensionality reduction by learning an invariant mapping, 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06) 2. IEEE, 2006.
Google Scholar
F. Schroff, K. Dmitry, P. James, Facenet: A unified embedding for face recognition and clustering, Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
DOI: https://doi.org/10.1109/CVPR.2015.7298682
Google Scholar
R. E. Saragih, Q. H. To, A survey of face recognition based on convolutional neural network, Indonesian Journal of Information Systems 4(2) (2022).
DOI: https://doi.org/10.24002/ijis.v4i2.5439
Google Scholar
S. Almabdy, E. Lamiaa, Deep convolutional neural network-based approaches for face recognition, Applied Sciences 9(20) (2019) 4397.
DOI: https://doi.org/10.3390/app9204397
Google Scholar
R. Chauhan, K.K. Ghanshala, R.C. Joshi, Convolutional neural network (CNN) for image detection and recognition, 2018 first international conference on secure cyber computing and communication (ICSCCC). IEEE, 2018.
DOI: https://doi.org/10.1109/ICSCCC.2018.8703316
Google Scholar
X. Zhou, et al., Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems, IEEE Transactions on Industrial Informatics 17.8 (2020) 5790-5798.
DOI: https://doi.org/10.1109/TII.2020.3047675
Google Scholar
S. Bell, K. Bala, Learning visual similarity for product design with convolutional neural networks, ACM transactions on graphics (TOG) 34(4) (2015) 1-10.
DOI: https://doi.org/10.1145/2766959
Google Scholar
D. Chicco, Siamese neural networks: An overview, Artificial neural networks (2021) 73-94.
DOI: https://doi.org/10.1007/978-1-0716-0826-5_3
Google Scholar
Y. Feng, et al., Triplet distillation for deep face recognition, 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020.
DOI: https://doi.org/10.1109/ICIP40778.2020.9190651
Google Scholar
R. Jafri, H. R. Arabnia, A survey of face recognition techniques, Journal of information processing systems 5(2) (2009) 41-68.
DOI: https://doi.org/10.3745/JIPS.2009.5.2.041
Google Scholar
R. Cabeza, et al., The prototype effect in face recognition: Extension and limits, Memory & cognition 27 (1999) 139-151.
DOI: https://doi.org/10.3758/BF03201220
Google Scholar
J. Galbally, M. Sébastien, F. Julian, Biometric antispoofing methods: A survey in face recognition, IEEE Access 2 (2014) 1530-1552.
DOI: https://doi.org/10.1109/ACCESS.2014.2381273
Google Scholar
H. J. Hsu, K. T. Chen, Face recognition on drones: Issues and limitations, Proceedings of the first workshop on micro aerial vehicle networks, systems, and applications for civilian use. 2015.
DOI: https://doi.org/10.1145/2750675.2750679
Google Scholar
Y. Dong, et al., Efficient decision-based black-box adversarial attacks on face recognition, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
DOI: https://doi.org/10.1109/CVPR.2019.00790
Google Scholar
Y. Zhong, D. Weihong, Towards transferable adversarial attack against deep face recognition, IEEE Transactions on Information Forensics and Security 16 (2020) 1452-1466.
DOI: https://doi.org/10.1109/TIFS.2020.3036801
Google Scholar
N. Mani, M. Melody, T.S. Moh, Defending deep learning models against adversarial attacks, International Journal of Software Science and Computational Intelligence (IJSSCI) 13(1) (2021) 72-89.
DOI: https://doi.org/10.4018/IJSSCI.2021010105
Google Scholar
J. Gozdur, B. Wiśniewski, P. Kopniak, Comparison of the effectiveness of selected face recognition algorithms for poor quality photos, Journal of Computer Sciences Institute 10 (2019) 67-70.
DOI: https://doi.org/10.35784/jcsi.210
Google Scholar
A. Cellerino, B. Davide, S. Ferdinando, Sex differences in face gender recognition in humans, Brain research bulletin 63(6) (2004) 443-449.
DOI: https://doi.org/10.1016/j.brainresbull.2004.03.010
Google Scholar
T. Baltrusaitis, P. Robinson, L. P. Morency, Constrained local neural fields for robust facial landmark detection in the wild, Proceedings of the IEEE international conference on computer vision workshops. 2013.
DOI: https://doi.org/10.1109/ICCVW.2013.54
Google Scholar
S. W. Arachchilage, E. Izquierdo, A framework for real-time face-recognition, 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019.
DOI: https://doi.org/10.1109/VCIP47243.2019.8965805
Google Scholar
T. Tuytelaars, K. Mikolajczyk, Local invariant feature detectors: a survey, Foundations and trends in computer graphics and vision 3(3) (2008) 177-280.
DOI: https://doi.org/10.1561/0600000017
Google Scholar
L. Shen, L. Bai, A review on Gabor wavelets for face recognition, Pattern analysis and applications 9 (2006) 273-292.
DOI: https://doi.org/10.1007/s10044-006-0033-y
Google Scholar
N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Vol. 1. IEEE, 2005.
Google Scholar
K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors, IEEE transactions on pattern analysis and machine intelligence 27(10) (2005) 1615-1630.
DOI: https://doi.org/10.1109/TPAMI.2005.188
Google Scholar
D. G. Lowe, Distinctive image features from scale-invariant keypoints, International journal of computer vision 60 (2004) 91-110.
DOI: https://doi.org/10.1023/B:VISI.0000029664.99615.94
Google Scholar
A. Naeem, et al., Malignant melanoma classification using deep learning: datasets, performance measurements, challenges and opportunities, IEEE access 8 (2020) 110575-110597.
DOI: https://doi.org/10.1109/ACCESS.2020.3001507
Google Scholar
R. Sujatha, et al., Performance of deep learning vs machine learning in plant leaf disease detection, Microprocessors and Microsystems 80 (2021) 103615.
DOI: https://doi.org/10.1016/j.micpro.2020.103615
Google Scholar
E. Hoffer, N. Ailon, Deep metric learning using triplet network. Similarity-Based Pattern Recognition: Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings 3. Springer International Publishing, 2015.
Google Scholar
P. Parveen, B. Thuraisingham, Face recognition using multiple classifiers, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06). IEEE, 2006.
DOI: https://doi.org/10.1109/ICTAI.2006.59
Google Scholar
J.R. Beveridge, et al., A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. Vol. 1. IEEE, 2001.
Google Scholar
P. Kamencay, et al., A new method for face recognition using convolutional neural network, Advances in Electrical and Electronic Engineering 15(4) (2017) 663-672.
DOI: https://doi.org/10.15598/aeee.v15i4.2389
Google Scholar
M. Abadi, TensorFlow: learning functions at scale, Proceedings of the 21st ACM SIGPLAN international conference on functional programming. 2016.
DOI: https://doi.org/10.1145/2951913.2976746
Google Scholar
J. V. Dillon, et al., Tensorflow distributions, arXiv preprint arXiv:1711.10604 (2017).
Google Scholar
B. Pang, E. Nijkamp, Y N Wu, Deep learning with tensorflow: A review, Journal of Educational and Behavioral Statistics 45(2) (2020) 227-248.
DOI: https://doi.org/10.3102/1076998619872761
Google Scholar
F.S Samaria, A.C Harter, Parameterisation of a stochastic model for human face identification, In Proceedings of 1994 IEEE workshop on applications of computer vision. IEEE, (1994) 138-142.
Google Scholar
Authors
Makrem Beldibeldimakrem@gmail.com
The National Engineering School of Tunis or ENIT, Tunisia Tunisia
Statistics
Abstract views: 145PDF downloads: 138
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.