Deep Learning Based Age Estimation Model

Title

Deep Learning Based Age Estimation Model

Creator

Reddy G.J.; Bendili S.; Vinodha D.; Jenefa J.; Sambandam R.K.; Vetriveeran D.

Description

To improve accuracy and resilience in demographic categorization, this research presents a novel use of Convolutional Neural Networks (CNNs) for age prediction. Deep learning is utilized to achieve this goal. Precise estimation of age has become essential in a variety of areas, including human-computer interaction, marketing, and healthcare. The ability of CNNs to handle the intricacies of facial features for accurate demographic forecasts is examined in this study. The research covers every step of the age prediction process, including dataset collection, prepossessing, model architecture, and assessment measures. The CNN is trained to automatically extract hierarchical characteristics from facial photos, which enables the model to recognize complex patterns related to age. The architecture's flexibility to different lighting conditions, facial expressions, and postures. In this research, we deal with deep learning-based perceived age estimation in still-face pictures. Our Convolution Neural Network models (CNNs) have been trained prior on Image Net for picture classification, as they use the VGG architecture. In addition, we analyze the effects of tailoring over Web photos having known age, considering a lack of apparent age-annotated annotated images. In addition, this work adds to the increasing library of studies on the use of deep learning methods for demographic data evaluation by showing the effectiveness of CNNs to predict age. The results show how, in practical situations, CNNs could significantly enhance the precision and dependability of age prediction systems. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Accuracy; convolutional neural network (CNN); Data Augmentation; Machine learning algorithm; Mean absolute error; Preprocessing; RELU

Coverage

Reddy G.J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Bendili S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vinodha D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Jenefa J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Sambandam R.K., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vetriveeran D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545269

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204482593&doi=10.1109%2fTQCEBT59414.2024.10545269&partnerID=40&md5=13551662f0d49d361ac40ccbfeca5911