Human Age and Gender Estimation using Convolutional Neural Network and Image Processing
Objective
The objective of this project is to develop a reliable and accurate system for human age and gender estimation using convolutional neural network (CNN) and image processing techniques. The main goal is to design a model that can accurately estimate the age and gender of individuals based on their facial features.
Abstract
In recent years, age estimation and gender classification were one of the issues most frequently discussed in the field of pattern recognition and computer vision. This project proposes automated predictions of age and gender-based features extraction from human facials images. Contrary to the other conventional approaches on the unfiltered face image, in this study, we show that a substantial improvement be obtained for these tasks by learning representations with the use of the deep convolutional neural networks (CNN). The convolutional neural network method used in this research enhances robustness for highly variable unconstrained recognition tasks to identify the gender and age group estimation. This research was analyzed and validated for the gender prediction and age estimation on the face dataset from different sources. The results obtained show that the proposed approach offers a major performance gain, our model achieve very interesting efficiency and the state-of-the-art performance in both age and gender scoring.
Keywords: Image preprocessing, gender prediction, age estimation, deep neural network, convolutional neural networks (CNN)
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2018a or above
Hardware:
Operating Systems:
· Windows 10
· Windows 7 Service Pack 1
· Windows Server 2019
· Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
