IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning

Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning
View Sample PDF
Author(s): Yogesh M. Kamble (DKTE Society's Textile and Engineering Institute, Ichalkaranji, India)and Raj B. Kulkarni (Government College of Engineering, Karad, India)
Copyright: 2024
Volume: 12
Issue: 1
Pages: 10
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.309960

Purchase

View Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning on the publisher's website for pricing and purchasing information.

Abstract

Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very impressive performances. In general, deep architecture models suffer from the over-fitting problem when there is a small number of training data. In this article the attempt is made to remedy this problem in deep architecture with regularization techniques including overlap pooling and flipped image augmentation and dropout; the authors also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. The authors present a hybrid neural-network solution which compares favorably with other methods.

Related Content

Yogesh M. Kamble, Raj B. Kulkarni. © 2024. 10 pages.
Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 15 pages.
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 14 pages.
Partha Ghosh, Takaaki Goto, Leena Jana Ghosh, Giridhar Maji, Soumya Sen. © 2024. 15 pages.
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi. © 2024. 14 pages.
Kuo Jong-Yih, Hsieh Ti-Feng, Lin Yu-De, Lin Hui-Chi. © 2024. 17 pages.
. © 2024.
Body Bottom