Design of a Face Recognition System based on Convolutional Neural Network (CNN)
Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.
Keywords:face recognition, biometrics, Convolutional Neural Networks (CNNs), artificial intelligence, deep learning
R. Ayachi, M. Afif, Y. Said, M. Atri, “Traffic signs detection for real-world application of an advanced driving assisting system using deep learning”, Neural Processing Letters, Vol. 51, pp. 837-851, 2020 DOI: https://doi.org/10.1007/s11063-019-10115-8
R. Ayachi, Y. E. Said, M. Atri, “To perform road signs recognition for autonomous vehicles using cascaded deep learning pipeline”, Artificial Intelligence Advances, Vol. 1, No. 1, pp. 1-58, 2019 DOI: https://doi.org/10.30564/aia.v1i1.569
M. Afif, R. Ayachi, Y. Said, E. Pissaloux, M. Atri, “Indoor image recognition and classification via deep convolutional neural network”, 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, Maghreb, Tunisia, December 18-20, 2018
M. Afif, R. Ayachi, Y. Said, E. Pissaloux, M. Atri, “An evaluation of retinanet on indoor object detection for blind and visually impaired persons assistance navigation”, Neural Processing Letters, Vol. 51, pp. 1-15, 2020 DOI: https://doi.org/10.1007/s11063-020-10197-9
M. Afif, R. Ayachi, Y. Said, E. Pissaloux, M. Atri, “Indoor object c1assification for autonomous navigation assistance based on deep CNN model”, IEEE International Symposium on Measurements & Networking, Catania, Italy, July 8-10, 2019 DOI: https://doi.org/10.1109/IWMN.2019.8805042
D. Virmani, P. Girdhar, P. Jain, P. Bamdev, “FDREnet: Face detection and recognition pipeline”, Engineering, Technology & Applied Science Research, Vol. 9, No. 2, pp. 3933-3938, 2019 DOI: https://doi.org/10.48084/etasr.2492
U. Khan, K. Khan, F. Hasssan, A. Siddiqui, M. Afaq, “Towards achieving machine comprehension using deep learning on non-GPU machines”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4423-4427, 2019 DOI: https://doi.org/10.48084/etasr.2734
H. M. Moon, C. H. Seo, S. B. Pan, “A face recognition system based on convolution neural network using multiple distance face”, Soft Computing, Vol. 21, pp. 4995-5002, 2017 DOI: https://doi.org/10.1007/s00500-016-2095-0
H. Khalajzadeh, M. Manthouri, M. Teshnehlab, “Face recognition using convolutional neural network and simple logistic classifier”, Advances in Intelligent Systems and Computing, Vol. 223, pp. 197-207, 2014 DOI: https://doi.org/10.1007/978-3-319-00930-8_18
Yale Face Database, available at: http://vision.ucsd.edu/content/yale-face-database
K. Yan, S. Huang, Y. Song, W. Liu, N. Fan, “Face recognition based on convolution neural network”, 36th Chinese Control Conference, Dalian, China, July 26-28, 2017 DOI: https://doi.org/10.23919/ChiCC.2017.8027997
AT&T Database of Faces: ORL face database, available at: http://cam-orl.co.uk/facedatabase.html
A. Martinez, R. Benavente, The AR face database, technical report series, CVC, 1998
L. Li, Z. Jun, J. Fei, S. Li, “An incremental face recognition system based on deep learning”, Fifteenth IAPR International Conference on Machine Vision Applications, Nagoya, Japan, May 8-12, 2017 DOI: https://doi.org/10.23919/MVA.2017.7986845
F. Schroff, D. Kalenichenko, J. Philbin, “Facenet: A unified embedding for face recognition and clustering”, 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, June 7-12, 2015 DOI: https://doi.org/10.1109/CVPR.2015.7298682
M. Nakada, H. Wang, D. Terzopoulos, “AcFR: Active face recognition using convolutional neural networks”, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, July 21-26, 2017 DOI: https://doi.org/10.1109/CVPRW.2017.11
R. Gross, I. Matthews, J. Cohn, T. Kanade, S. Baker, “Multi-pie”, Image and Vision Computing, Vol. 28, No. 5, pp. 807-813, 2010 DOI: https://doi.org/10.1016/j.imavis.2009.08.002
J. Li, T. Qiu, C. Wen, K. Xie, F. Q. Wen, “Robust face recognition using the deep C2D-CNN model based on decision-level fusion”, Sensors, Vol. 18, pp. 1-27, 2018 DOI: https://doi.org/10.3390/s18072080
F. Taherkhani, N. M. Nasrabadi, J. Dawson. “A deep face identification network enhanced by facial attributes prediction”, IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23, 2018 DOI: https://doi.org/10.1109/CVPRW.2018.00097
K. Guo, S. Wu, Y. Xu, “Face recognition using both visible light image and near-infrared image and a deep network”, CAAI Transactions on Intelligence Technology, Vol. 2, No. 1, pp. 39-47, 2017 DOI: https://doi.org/10.1016/j.trit.2017.03.001
A. Shraddha, A. Agrawal, “Face recognition with partial face recognition and convolutional neural network”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 7, No. 1, pp. 91-94, 2018
P. Kamencay, M. Benco, T. Mizdos, R. Radil, “A new method for face recognition using convolutional neural network”, Digital Image Processing and Computer Graphics, Vol. 15, No. 4, pp. 663-672, 2017 DOI: https://doi.org/10.15598/aeee.v15i4.2389
A. Krizhevsky, I. Sutskever, G. E. Hinton, “Imagenet classification with deep convolutional neural networks”, Advances in Neural Information Processing Systems, Vol. 25, No. 2, pp. 1097-1105, 2012
M. H. Yang, “Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods”, 5th International Conference on Automatic Face Gesture Recognition, Washington, USA, May 21-21, 2002 DOI: https://doi.org/10.1109/AFGR.2002.4527207
J. Yang, D. Zhang, A. F. Frangi, J. Y. Yang, “Two-dimensional PCA: A new approach to appearance-based face representation and recognition”, IEEE Transactionson Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, pp. 131-137, 2004 DOI: https://doi.org/10.1109/TPAMI.2004.1261097
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