A truncated deep neural network for identifying age groups in real time images

Gupta, Vedika, Dass, Pranav, Bansal, Vibhuti and Arora, Rameshwar (2022) A truncated deep neural network for identifying age groups in real time images. Journal of Interdisciplinary Mathematics, 25 (3). pp. 851-861. ISSN 2169-012X

[thumbnail of A truncated deep neural network for identifying age groups in real time images.pdf] Text
A truncated deep neural network for identifying age groups in real time images.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

Recent research works have been focussing on estimating age from facial images. Age estimation from faces basically involves two sub-processes: extracting features and estimating learning function. Age classification from an input image is the task at hand in this project; age will be classified into 3 categories: 1) Toddler, 2) Teen, 3) Adult. Classifying age automatically from an image has been widely used in our day-to-day lives, particularly in the listed fields: biometrics, surveillance systems, and commercial kiosks. The purpose of this study is to categorize facial images based on their age. Prominently, previously existing research works were performed on contrived and unreal images curated in laboratories. Those images did not correctly portray the distinctions and fluctuations that are evident in real human faces. This paper uses deep convolutional neural networks (CNN) on the available data to overcome the above discussed challenge.

Item Type: Article
Keywords: Mask regional convolutional neural network (Mask RCNN) | Deep convolutional neural network (DCNN) | Residual network (ResNet)
Subjects: Physical, Life and Health Sciences > Mathematics
JGU School/Centre: Jindal Global Business School
Depositing User: Shilpi Rana
Date Deposited: 07 Feb 2022 05:37
Last Modified: 22 Feb 2023 07:22
Official URL: https://doi.org/10.1080/09720502.2021.2016917
URI: https://pure.jgu.edu.in/id/eprint/1069

Downloads

Downloads per month over past year

Actions (login required)

View Item
View Item