Visual Mapping for Gender Identification from Facial Images using BAM and DON

Shoba Rani Salvadi; D Nagendra Rao; S Vathsal

doi:10.17485/IJST/v16i17.349

Article

Visual Mapping for Gender Identification from Facial Images using BAM and DON

VIEWS 420
PDF 127

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i17.349

Year: 2023, Volume: 16, Issue: 17, Pages: 1295-1301

Original Article

Visual Mapping for Gender Identification from Facial Images using BAM and DON

Shoba Rani Salvadi^1*, D Nagendra Rao², S Vathsal³

¹Research scholar, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India
²Professor and Principal, Abhinav-Hitech College of Engineering, Moinabad, Telangana, India
³Retired DRDO Scientist

*Corresponding Author
Email: [email protected]

Received Date:17 February 2023, Accepted Date:12 March 2023, Published Date:02 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To Develop an intelligent and innovative method to categorize the Gender by focusing facial images. Method: We integrate the characteristics of Bidirectional Associative Memory (BAM) and Deep Octonion Networks (DON) to enhance the Gender detection in real time applications. The developed hybrid model is called Visual Mapping of BAM and DON (VMBAD). To validate the projected system, we make use of 4000 images and 126 different subjects as a data set to train the proposed approach and simultaneously compare our results with the existing methods using the same data set. Findings: The projected technique improves the performance of the system by 3 -5 % in terms of sensitivity, accuracy, and precision when compared with the existing approaches (vide figures 4-7). Novelty: The designed method enhances both the accuracy and precision of image by nearly 4% and 2% respectively when compared with the reported work.

Keywords: Artificial Intelligence; Bidirectional Associative Memories; Gender Identification; Deep Octonion Networks; Deep Quaternion Networks

References

Comminiello D, Scarpiniti M, Parisi R, Uncini A. Frequency-domain Adaptive Filtering: from Real to Hypercomplex Signal Processing. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. 7745-7749) IEEE. 2019.
Labunets VG, Smetanin JG, Chasovskikh VP, Ostheimer E. Hypercomplex Algebras as Unified Language for Image Processing and Pattern Recognition Part 1. Cliffordean Models of Multichannel Images. In: Innovation and Discovery in Russian Science and Engineering. (pp. 3-19) Springer International Publishing. 2020.
Wang C, Hao Q, Ma B, Wu X, Li J, Xia Z, et al. Octonion continuous orthogonal moments and their applications in color stereoscopic image reconstruction and zero-watermarking. Engineering Applications of Artificial Intelligence. 2021;106:104450. Available from: https://doi.org/10.1016/j.engappai.2021.104450
Schwarzenberg P, Figueroa A. Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members. IEEE Access. 2023;11:3983–3995. Available from: https://doi.org/10.1109/ACCESS.2023.3235735
Gao WBB, Li BZZ. The octonion linear canonical transform: Definition and properties. Signal Processing. 2021;188:108233. Available from: https://doi.org/10.1016/j.sigpro.2021.108233
Cariow A, Cariowa G, Majorkowska-Mech D. An Algorithm for Quaternion-Based 3D Rotation. International Journal of Applied Mathematics and Computer Science. 2020;30:149–160. Available from: https://doi.org/10.34768/amcs-2020-0012
Jose JA, Kumar CS, Sureshkumar S. Region-Based Split Octonion Networks with Channel Attention Module for Tuna Classification. International Journal of Pattern Recognition and Artificial Intelligence. 2022;36(07):2250030. Available from: https://doi.org/10.1142/S0218001422500306
Chouhan SS, Kumar R, Sarkar S, Das S. Multistability analysis of octonion-valued neural networks with time-varying delays. Information Sciences. 2022;609:1412–1434. Available from: https://doi.org/10.1016/j.ins.2022.07.123
Takahashi K, Fujita M, Hashimoto M. Remarks on Octonion–valued Neural Networks with Application to Robot Manipulator Control. In: IEEE International Conference on Mechatronics (ICM). (pp. 1-6) IEEE. 2021.
Cariow A, Cariowa G. Fast Algorithms for Quaternion-Valued Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 2021;32(1):457–462. Available from: https://doi.org/10.1109/TNNLS.2020.2979682
Wu J, Xu L, Wu F, Kong Y, Senhadji L, Shu H. Deep octonion networks. 2020. Available from: https://doi.org/10.1016/j.neucom.2020.02.053
Rasheed J, Waziry S, Alsubai S, Abu-Mahfouz AM. An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces. Processes. 1427;10(7):1427. Available from: https://doi.org/10.3390/pr10071427
Rani RM, Pushpalatha M. Generation of Frequent sensor epochs using efficient Parallel Distributed mining algorithm in large IOT. Computer Communications. 2019;148:107–114. Available from: https://doi.org/10.1016/j.comcom.2019.09.006
Garain A, Ray B, Singh PK, Ahmadian A, Senu N, Sarkar R. GRA_Net: A Deep Learning Model for Classification of Age and Gender From Facial Images. IEEE Access. 2021;9:85672–85689. Available from: https://doi.org/10.1109/ACCESS.2021.3085971
Abirami B, Subashini TS, Mahavaishnavi V. Gender and age prediction from real time facial images using CNN. Materials Today: Proceedings. 2020;33:4708–4712.
Abdolrashidi A, Minaei M, Azimi E, Minaee S. Age and gender prediction from face images using attentional convolutional network. 2020. Available from: https://doi.org/10.48550/arXiv.2010.03791
Ghildiyal A, Sharma S, Verma I, Marhatta U. Age and Gender Predictions using Artificial Intelligence Algorithm. 3rd International Conference on Intelligent Sustainable Systems (ICISS). 2020;p. 371–375. Available from: https://doi.org/10.1109/ICISS49785.2020.9316053
Gupta SK, Nain N. Review: Single attribute and multi attribute facial gender and age estimation. Multimedia Tools and Applications. 2023;82(1):1289–1311. Available from: https://doi.org/10.1007/s11042-022-12678-6
Swaminathan A, Chaba M, Sharma DK, Chaba Y. Gender Classification using Facial Embeddings: A Novel Approach. Procedia Computer Science. 2020;167:2634–2642. Available from: https://doi.org/10.1016/j.procs.2020.03.342
Xiao J, Guo X, Li Y, Wen S, Shi K, Tang Y. Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks. Neural Networks. 2022;154:491–507. Available from: https://doi.org/10.1016/j.neunet.2022.07.031

Copyright

© 2023 Salvadi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)