International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.14, No.3, Jun. 2022
Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization
Full Text (PDF, 1845KB), PP.32-46
Technology is getting smarter day by day and facilitating every part of human life from automatic alarming, automatic temperature, and personalised choice prediction and behaviour recognition. Such technological advancements are using different machine learning techniques for artificial intelligence. Face recognition is also one of the techniques to develop futuristic artificial intelligence-based technology used to get devices equipped with personalised features and security. Face recognition is also used for keeping information of facial data of employees of any company citizens of any country to get tracked and control over crimes in unfair incidents. For making face recognition more reliable and faster, several techniques are evolving every day. One of the fastest and most dependable face recognitions is CNN based face recognition. This work is designed based on the multiple convolutional module-based CNN equipped with batch normalisation and linear rectified unit for normalising and optimising features with minibatch. Faces in CNN’s fully connected layer are classified using the SoftMax classifier. The ORL and Yale face datasets are used for training. The average accuracy achieved is 94.74% for ORL and 96.60% for Yale Datasets. The convolutional neural network training was done for different training percentages, e.g., 66%, 67%, 68%, 69%, 70%, and 80%. The experimental outcomes exhibited that the defined approach had enhanced the face recognition performance.
Cite This Paper
Deepa Indrawal, Archana Sharma, "Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.3, pp. 32-46, 2022.DOI: 10.5815/ijigsp.2022.03.04
M. A. Talab, S. Awang, and S. A. M. Najim, “Super-Low Resolution Face Recognition using Integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN),” in 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Selangor, Malaysia, Jun. 2019, pp. 331–335.
D. N. Parmar and B. B. Mehta, “Face Recognition Methods & Applications,” Int. J. Comput. Technol. Appl., vol. 4, no. 1, pp. 84–86, 2013.
T. Meenpal, A. Balakrishnan, and A. Verma, “Facial Mask Detection using Semantic Segmentation,” in 2019 4th International Conference on Computing, Communications, and Security (ICCCS), Rome, Italy, Oct. 2019, pp. 1–5.
M. M. Y. Zhang, K. Shang, and H. Wu, “Learning deep discriminative face features by customised weighted constraint,” Neurocomputing, vol. 332, pp. 71–79, Mar. 2019.
D. Bhamare and P. Suryawanshi, “Review on Reliable Pattern Recognition with Machine Learning Techniques,” Fuzzy Information and Engineering, vol. 10, no. 3, pp. 362–377, Jul. 2018.
Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, 40135, Indonesia et al., “Comparing Performance of Supervised Learning Classifiers by Tuning the Hyperparameter on Face Recognition,” IJISA, vol. 13, no. 5, pp. 1–13, Oct. 2021.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep Face Recognition,” in Proceedings of the British Machine Vision Conference 2015, Swansea, 2015.
G. Guo and N. Zhang, “A survey on deep learning-based face recognition,” Computer Vision and Image Understanding, vol. 189, p. 102805, Dec. 2019.
Ming Liang and Xiaolin Hu, “Recurrent convolutional neural network for object recognition,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 3367–3375.
P. O. Pinheiro and R. Collobert, “Recurrent Convolutional Neural Networks for Scene Labeling,” p. 9, 2014.
Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, and Z. Zhang, “DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 3982–3991.
M. You, X. Han, Y. Xu, and L. Li, “Systematic evaluation of deep face recognition methods,” Neurocomputing, vol. 388, pp. 144–156, May 2020.
Research Scholar, Department of ECE, Global Academy of Technology, Bangalore-560098, R. K, and R. J, “Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features,” IJCNIS, vol. 13, no. 1, pp. 47–60, Feb. 2021.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017.
A. Uçar, Y. Demir, and C. Güzeliş, “Object recognition and detection with deep learning for autonomous driving applications,” SIMULATION, vol. 93, no. 9, pp. 759–769, Sep. 2017.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” 2015.
Z. Cui, H. Chang, S. Shan, B. Zhong, and X. Chen, “Deep Network Cascade for Image Super-resolution,” in Computer Vision – ECCV 2014, vol. 8693, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 49–64. Accessed: Mar. 10, 2022.
E. Zangeneh, M. Rahmati, and Y. Mohsenzadeh, “Low-Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,” 2017.
G. Gao et al., “Robust low-resolution face recognition via low-rank representation and locality-constrained regression,” Computers & Electrical Engineering, vol. 70, pp. 968–977, Aug. 2018.
R. Tkachenko, P. Tkachenko, I. Izonin, and Y. Tsymbal, “Learning-Based Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm,” in Advances in Soft Computing and Machine Learning in Image Processing, vol. 730, A. E. Hassanien and D. A. Oliva, Eds. Cham: Springer International Publishing, 2018, pp. 537–565. Accessed: Mar. 10, 2022.
I. Izonin, R. Tkachenko, D. Peleshko, T. Rak, and D. Batyuk, “Learning-based image super-resolution using weight coefficients of synaptic connections,” in 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT), Lviv, Ukraine, Sep. 2015, pp. 25–29.
J. Mohammed Sahan, E. I. Abbas, and Z. M. Abood, “A facial recognition using a combination of a novel one dimension deep CNN and LDA,” Materials Today: Proceedings, p. S2214785321051841, Jul. 2021.
S. Banerjee and S. Das, “Mutual variation of information on transfer-CNN for face recognition with degraded probe samples,” Neurocomputing, vol. 310, pp. 299–315, Oct. 2018.