Distribution Model of Deep Learning (DDL) based Optimal Occluded Face Detection and Recognition

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : C.J. Harshitha, R.K. Bharathi

10.14445/23488549/IJECE-V12I6P109

How to Cite?

C.J. Harshitha, R.K. Bharathi, "Distribution Model of Deep Learning (DDL) based Optimal Occluded Face Detection and Recognition," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 106-118, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P109

Abstract:

Occluded face recognition based on the texture pattern is a major research topic in pattern recognition and authentication systems. The application in various areas of providing secure authentication, personal identification, and access control is another advantage of this recognition approach. Researchers have studied numerous image-processing strategies to improve recognition performance. However, all these methods have their limits, like low accuracy, low classification rate, and large mistake rate. This study recommends a pattern extraction-based classification method for texture pattern identification to address these issues and enhance classification performance. Firstly, we prepare the input test image using the Gaussian Neighborhood Difference (GND) method to remove and smooth noise effectively. Then, block separation is adopted to select the most representative patterns in the processed image using the Deep Ternary Pattern (DTP) method. The image is identified using the extracted feature vectors using a Distributed Deep Learning (DDL) classifier. The experimental result analysis proves the efficiency of this pattern extraction model in terms of comparison with other methods. The classification results have been compared with state-of-the-art approaches, improving prediction accuracy.

Keywords:

Deep Ternary Pattern (DTP), Batched Firefly (BFF) optimization, Distributed Deep Learning (DDL) and Recognition System, Gaussian Neighborhood Difference (GND).

References:

[1] Carson K. Leung, Fan Jiang, and Joglas Souza, “Web Page Recommendation from Sparse Big Web Data,” 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, pp. 592-597, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hua Chen, “Personalized Recommendation System of E-Commerce based on Big Data Analysis,” Journal of Interdisciplinary Mathematics, vol. 21, no. 5, pp. 1243-1247, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Gerard Deepak, and J. Sheeba Priyadarshini, “A Hybrid Semantic Algorithm for Web Image Retrieval Incorporating Ontology Classification and User-Driven Query Expansion,” Advances in Big Data and Cloud Computing, pp. 41-49, 2018. [CrossRef] [Google Scholar] [Publisher Link]
[4] J. Bhavithra, and A. Saradha, “Personalized Web Page Recommendation using Case-Based Clustering and Weighted Association Rule Mining,” Cluster Computing, vol. 22, pp. 6991-7002, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sandeep K. Raghuwanshi, and R.K. Pateriya, Collaborative Filtering Techniques in Recommendation Systems, Data, Engineering and Applications, pp. 11-21, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Nymphia Pereira, and Satishkumar L. Varma, “Financial Planning Recommendation System using Content-Based Collaborative and Demographic Filtering,” Smart Innovations in Communication and Computational Sciences, pp. 141-151, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Virendra Singh Rathore, and Nidhi Singh, “Plan of Proficient URL Based Web Page Classification Utilizing NLP,” International Journal of Research in Engineering, Science and Management, vol. 2, no. 10, pp. 119-121, 2019.
[Google Scholar] [Publisher Link]
[8] Charul Nigam, and A.K. Sharma, “Experimental Performance Analysis of Web Recommendation Model in Web usage Mining using KNN Page Ranking Classification Approach,” Materials Today: Proceedings, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Safae Lassri, El Habib Benlahmar, and Abderrahim Tragha, “Web Page Classification Based on an Accurate Technique for Key Data Extraction,” International Conference on Advanced Intelligent Systems for Sustainable Development, pp. 1124-1131, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mei Wang, and Weihong Deng, “Deep Face Recognition: A Survey,” Neurocomputing, vol. 429, pp. 215-244, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yaniv Taigman et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1701-1708, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] 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, pp. 815-823, 2015.
[Google Scholar] [Publisher Link]
[13] O. Parkhi, A. Vedaldi, and A. Zisserman, “Deep Face Recognition,” Proceedings of the British Machine Vision Conference, pp. 1-12, 2015.
[Google Scholar] [Publisher Link]
[14] Kaipeng Zhang et al., “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Iacopo Masi et al., “Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,” 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, pp. 579-596, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Jiankang Deng et al., “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 4685-4694, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Kaiming He et al., “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Weiyang Liu et al., “SphereFace: Deep Hypersphere Embedding for Face Recognition,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 212-220, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xiangyu Zhu et al., “High-Fidelity Pose and Expression Normalization for Face Recognition in the Wild,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 787-796, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Meina Kan, Shiguang Shan, and Xilin Chen, “Multi-View Deep Network for Cross-View Classification,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 4847-4855, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shengcai Liao, Anil K. Jain, and Stan Z. Li, “Partial Face Recognition: Alignment-Free Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1193-1205, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Zinelabidine Boulkenafet, Jukka Komulainen, and Abdenour Hadid, “Face Anti-Spoofing Based on Color Texture Analysis,” 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, pp. 2636-2640, 2015. [CrossRef] [Google Scholar] [Publisher Link]
[23] Rajeev Ranjan et al., “An All-in-One Convolutional Neural Network for Face Analysis,” 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, pp. 17-24, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Munawar Hayat, Mohammed Bennamoun, and Senjian An, “Deep Reconstruction Models for Image Set Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 4, pp. 713-727, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yangjun Mao et al., “Improving Reference-Based Distinctive Image Captioning with Contrastive Rewards,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 20, no. 12, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Dong Chen et al., “Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification,” 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp. 3025-3032, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Changxing Ding, and Dacheng Tao, “Robust Face Recognition via Multimodal Deep Face Representation,” IEEE Transactions on Multimedia, vol. 17, no. 11, pp. 2049-2058, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] George Trigeorgis et al., “Adieu Features? End-to-End Speech Emotion Recognition Using a Deep Convolutional Recurrent Network,” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 5200-5204, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Karen Simonyan, and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv, pp. 1-14, 2015.
[CrossRef] [Google Scholar] [Publisher Link]