Deep Learning Approach for Face Recognition Based on Multi-Layers CNN&SVM
Deep Learning Approach for Face Recognition Based on Multi-Layers CNN&SVM |
||
 |
 |
|
| © 2023 by IJETT Journal | ||
| Volume-71 Issue-8 |
||
| Year of Publication : 2023 | ||
| Author : Abu Sanusi Darma, Fatma Susilawati Mohamad, Oladapo Ayodeji Diekola, Ibrahim Mohammed Sulaiman |
||
| DOI : 10.14445/22315381/IJETT-V71I8P34 | ||
How to Cite?
Abu Sanusi Darma, Fatma Susilawati Mohamad, Oladapo Ayodeji Diekola, Ibrahim Mohammed Sulaiman, "Deep Learning Approach for Face Recognition Based on Multi-Layers CNN&SVM," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 388-409, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P234
Abstract
The inspiration behind the huge attention given to face recognition systems by the research community and computer vision specialists is the need to enhance face recognition systems' effectiveness, accuracy rate, and speed. The complexity of recognizing the human face by machines due to different variations in poses, illumination, age, facial expression, occlusion, personal appearance, and different cosmetic effects makes face recognition more challenging. However, this makes it difficult to implement a robust computational system. The study's main goal is to enhance the current deep learning approaches for face recognition applications using an enhanced and efficient hybrid deep learning method that involves multi-layer CNN and SVM. The model is encompassed with a newly developed middle block convolutional regularization algorithm (MBCRA) and a pre-activation batch normalization method for computational stability and convergence speed. The combination of both CNN and SVM enables the system to obtain more significant face features from the images of the proposed AS_Darmaset. The database has six classes of images. Each class contains face images with specific variation problems. The experimental results demonstrate that the multi-layer CNN+SVM has a 99.87% accuracy, and the comparative analysis shows that the proposed model is more resilient for face image classification under unconstrained settings than the most developed deep learning model for face recognition.
Keywords
Deep learning, Face recognition, Convolutional Neural Network, and Support Vector Machine.
References
[1] A. Vinay et al., “Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study,” Procedia Computer Science, vol. 57, pp. 650–659, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Syafeeza Ahmad Radzi et al., “A MATLAB-Based Convolutional Neural Network Approach for Face Recognition System,” Journal of Bioinformatics, Proteomics and Imaging Analysis, vol. 2, no. 1, pp. 1–5, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Mejda Chihaoui et al., “Face Recognition Using HMM-LBP,” Hybrid Intelligent Systems, vol. 420, pp. 249-258, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Mohammed Bennamoun, Yulan Guo, and Ferdous Sohel, “Feature Selection for 2D and 3D Face Recognition,” Wiley Encyclopedia of Electrical and Electronics Engineering, pp. 1–28, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Lei Chen et al., “Face Recognition with Statistical Local Binary Patterns,” International Conference on Machine Learning and Cybernetics, pp. 2433–2439, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman, “Deep Face Recognition,” Proceedings of the British Machine Vision Conference (BMVC), 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Tsung-Yi Lin et al., “Microsoft COCO: Common Objects in Context,” European Conference on Computer Vision, vol. 8693, pp. 740–755, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Florian Schroff, Dmitry Kalenichenko, and James Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823, 2015.
[Google Scholar] [Publisher Link]
[9] Sanusi Darma Abu, and Fatma Susilawati Mohamad, “Approaches of Deep Learning in Persuading the Contemporary Society for the Adoption of New Trend of AI Systems: A Review,” International Journal Of Scientific & Technology Research, vol. 9, no. 12, pp. 163–177, 2020.
[Google Scholar] [Publisher Link]
[10] Zahraddeen Sufyanu et al., “Feature Extraction Methods for Face Recognition,” International Review of Applied Engineering Research (IRAER), vol. 5, no. 3, pp. 5658-5668, 2016.
[Google Scholar] [Publisher Link]
[11] Tejaswi Satepuri, and P. Chandrasekar Reddy, “A Survey on Facial Expression Recognition Techniques,” International Journal of Computer Sciences and Engineering, vol. 7, no. 5, pp. 980–984, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Umara Zafar et al., “Face Recognition with Bayesian Convolutional Networks for Robust Surveillance Systems,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 10, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Anandhavalli Muniasamy, and Areej Alasiry, “Deep Learning: The Impact on Future eLearning,” International Journal of Emerging Technologies in Learning, vol. 15, no. 1, pp. 188–199, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] A. M. Turing, “Computing Machinery and Intelligence,” The Mind Association, vol. LIX, no. 236, pp. 433–460, 1950.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Shanshan Guo, Shiyu Chen, and Yanjie Li, “Face Recognition Based on Convolutional Neural Network and Support Vector Machine,” IEEE International Conference on Information and Automation (ICIA), pp. 1787-1792, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Firoz Mahmud et al., “PCA and Back-Propagation Neural Network-Based Face Recognition System,” 18th International Conference on Computer and Information Technology (ICCIT), pp. 582–587, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mohannad Abuzneid, and Ausif Mahmood, “Improving Human Face Recognition Using Deep Learning Based Image Registration and Multi-Classifier Approaches,” IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1-2, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Peibo Duan et al., “Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm,” arXiv, Multiagent Systems, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mohannad A. Abuzneid, and Ausif Mahmood, “Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and Back-Propagation Neural Network,” IEEE Access, vol. 6, pp. 20641–20651, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Li Guo et al., “Face Image Classification Using Appearance and Texture Features,” International Conference on Computer Application and System Modeling, pp. 476–480, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Matthew C. Fysh, and Markus Bindemann, “Human-Computer Interaction in Face Matching,” Cognitive Science, vol. 42, no. 5, pp. 1714-1732, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] LeCun et al., “A B7CEDGF HIB7PRQTSUDGQICWVYX HIB edCdSISIXvg5r CdQTw XvefCdS,” Proceeding IEEE, 1998.
[Google Scholar]
[23] Teofilo F. Gonzalez, Handbook of Approximation Algorithms and Metaheuristics, 1st Edition, Chapman and Hall/CRC, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mohammed Kamel Benkaddour, and Abdennacer Bounoua, “Feature Extraction and Classification Using Deep Convolutional Neural Networks, PCA and SVC for Face Recognition,” Traitement du Signal, vol. 34, no. 1–2, pp. 77–91, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] T. Kujani, and V. Dhilip Kumar, “Emotion Understanding from Facial Expressions using Stacked Generative Adversarial Network (GAN) and Deep Convolution Neural Network (DCNN),” International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 98-110, 2022.
[CrossRef] [Publisher Link]
[26] Samar S. Mohamed et al., “Deep Learning Face Detection and Recognition,” International Journal of Electronics and Telecommunications, pp. 1–7, 2019.
[Google Scholar] [Publisher Link]
[27] Hayder Najm, Hayder Ansaf, and Oday A. Hassen, “An Effective Implementation of Face Recognition Using Deep Convolutional Network,” Journal of Southwest Jiaotong University, vol. 54, no. 5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Urvashi Bakshi, and Rohit Singhal, “A Survey on Face Detection Methods and Feature Extraction Techniques of Face Recognition,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 3, no. 3, pp. 233–237, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Mohammed Abdallh Otair, and A. Salameh Walid, “Efficient Training of Backpropagation Neural Networks,” Neural Network World, vol. 16, no. 4, pp. 291–311, 2015.
[Google Scholar] [Publisher Link]
[30] Hesham M. Eraqi, Mohamed N. Moustafa, and Jens Honer, “End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,” Machine Learning, arXiv, pp. 1–8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[31] A. R. Syafeeza et al., “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation,” International Journal of Engineering and Technology, vol. 6, no. 1, pp. 44–57, 2014.
[Google Scholar] [Publisher Link]
[32] Yaniv Taigman et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Vivienne Sze et al., “Efficient Processing of Deep Neural Networks: A Tutorial and Survey,” Proceedings of the IEEE, vol. 105, no. 12, pp. 2295–2329, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Wei Wang et al., “Development of Convolutional Neural Network and its Application in Image Classification: A Survey,” Optical Engineering, vol. 58, no. 4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Moacir Antonelli Ponti et al., “Everything You Wanted to Know About Deep Learning for Computer Vision but Were Afraid to Ask,” SIBGRAPI Conference on Graphics, Patterns and Images Tutorials, pp. 17–41, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[36] J. Wolfe et al., “Application of Softmax Regression and its Validation for Spectral-Based Land Cover Mapping,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 455–459, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[37] S.H. Shabbeer Basha et al., “Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification,” Neurocomputing, vol. 378, pp. 112–119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Gary B. Huang et al., “Labeled Faces in the Wild : A Database for Studying Face Recognition in Unconstrained Environments,” Artificial Intelligence, pp. 1–11, 2008.
[Google Scholar] [Publisher Link]
[39] Imokhai T. Tenebe et al., “Bacterial Contamination Levels and Brand Perception of Sachet Water: A Case Study in Some Nigerian Urban Neighborhoods,” MDPI Water, vol. 15, no. 9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Norah Alnaim, Maysam Abbod, and Rafiq Swash, “Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks,” Technologies, vol. 8, no. 2, p. 19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Nicole Christoff et al., “Morphological Crater Classification via Convolutional Neural Network with Application on MOLA data,” IEEE Advances in Neural Networks and Applications, pp. 1-5, 2018.
[Google Scholar] [Publisher Link]
[42] Bernd Fritzke “A Self-Organizing Network that Can Follow Non-Stationary Distributions,” Artificial Neural Networks — ICANN'97, vol. 1327, pp. 613–618, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Changzhi Bai et al., “Classification of Gas Dispersion States via Deep Learning Based on Images Obtained from a Bubble Sampler,” Chemical Engineering Journal Advances, vol. 5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Onome Christopher Edo et al., “Why do Healthcare Workers Adopt Digital Health Technologies- A Cross-Sectional Study Intergrating the TAM and UTAUT Model in a Developing Economy,” International Journal of Information Management Data Insights, vol. 3, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Joyassree Sen et al., “Face Recognition Using Deep Convolutional Network and One-shot Learning,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 23-29, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Cheng Xing, Jie-Sheng Wang, and Bo-wen Zheng, “Hybrid Face Recognition Method Based on Gabor Wavelet Transform and VGG Convolutional Neural Network with Improved Pooling Strategy,” IAENG International Journal of Computer Science, vol. 48, no. 2, pp. 1–14, 2021.
[Google Scholar] [Publisher Link]
[47] Ting-ting Yang, Su-yin Zhou, and Ai-jun Xu, “Rapid Image Detection of Tree Trunks Using a Convolutional Neural Network and Transfer Learning,” IAENG International Journal of Computer Science, vol. 48, no. 2, pp. 1–8, 2021.
[Google Scholar] [Publisher Link]
[48] Abu Sanusi Darma, and Susilawati Fatima Mohmad, “The Regularization Effect of Pre-Activation Batch Normalization on Convolutional Neural Network Performance for Face Recognition System Paper,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 11, pp. 300–310, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Zahraddeen Sufyanu, Fatma Susilawati Mohamad, and Ahmad Salihu Ben-Musa, “A Proposed Integrated Human Recognition for Security Reassurance,” American Journal of Applied Sciences, vol. 12, no. 2, pp. 155–165, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Mokhairi, Makhtar et al., “Comparison of Image Classification Techniques Using Caltech 101 Dataset,” Journal of Theoretical and Applied Information Technology, vol. 71, no. 1, pp. 79–86, 2015.
[Google Scholar] [Publisher Link]
[51] Patrik Kamencay et al., “A New Method for Face Recognition Using Convolutional Neural Network,” Advance in Electrical and Electronic Engineering, vol. 15, no. 4, pp. 663-672, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[52] M. Rajeshwari, and K. Rathika, “Palm Print Recognition Using Texture and Shape Features,” International Journal of Computer Science and Engineering, vol. 9, no. 2, pp. 1–5, 2022.
[Google Scholar] [Publisher Link]
[53] D. J. Samatha Naidu, and R. Lokesh, “Missing Child Identification System using Deep Learning with VGG-FACE Recognition Technique,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 9, pp. 1-11, 2022.
[CrossRef] [Publisher Link]
[54] D. M. Leppinen, and S. B. Dalziel, “Bubble Size Distribution in Dissolved Air Flotation Tanks,” Journal of Water Supply: Research and Technology-Aqua, vol. 53, no. 8, pp. 531–543, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Raja Durratun Safiyah et al., “Performance Evaluation for Vision-Based Vehicle Classification Using Convolutional Neural Network,” International Journal of Engineering & Technology, vol. 7, pp. 86–90, 2018.
[Google Scholar] [Publisher Link]
[56] R. Lemlich, “Foam Fractionation and Allied Techniques,” Industrial & Engineering Chemistry Research, vol. 60, no. 10, pp. 16–29, 2015.
[Google Scholar]