 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: July 2026
Volume 11 | Issue 7
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 122
Article Submitted : 25330
Article Published : 9497
Total Authors : 25208
Total Reviewer : 873
Total Countries : 172
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | A Comprehensive Review of Deep and Hybrid Machine Learning Algorithms for Age and Gender Prediction |
| Authors Name: | Krishna Vamshi , Neha Saravanan , Shreya Nagendra , Prashant P Patavardhan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_213624
|
| Published Paper Id: | IJRTI2606117 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | Computer vision based age and gender prediction has become essential in fields such as security, healthcare and social media analytics. This review examines key studies (2016–2024) that employ deep and hybrid machine learning models using facial and ocular data. The explored architectures include Convolutional Neural Networks (CNNs), Residual Attention Networks (GRA_Net), CAFFE- modified MobileNet V2 (CMNV2), Deep Class-Encoders, Deep Iris CNNs ,hybrid CNN–SVM and IoT-based systems. Experimental results across major datasets—Adience, FG-NET, UTKFace, AFAD, CASIA-WebFace and ND- GFI—show gender classification accuracies ranging from 91% to 99.8%, with Mean Abso-lute Error (MAE) for age estimation as low as 2.39 years. Eye- focused CNNs achieved up to 98.8% accuracy, highlighting the potential of ocular modalities. The paper synthesizes advances in network design, preprocessing and hybrid integration while addressing persistent challenges, including dataset bias, model interpretability and real-world deployment feasibility. Recent review studies highlight hybrid learning challenges under occlusion and device variability. |
| Keywords: | Age estimation · Gender prediction · Deep learning · Hybrid machine learning ·Convolutional Neural Network (CNN) · Residual networks · Attention mechanism · Internet of Things(IoT)· Biometrics |
| Cite Article: | "A Comprehensive Review of Deep and Hybrid Machine Learning Algorithms for Age and Gender Prediction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.b98-b106, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606117.pdf |
| Downloads: | 00082 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2606117
Registration ID:213624
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: b98-b106 Country: Bangalore, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606117 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606117 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||