Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/22172
Title: Face, age and gender identification system for application
Advisor : Sirisup Laohakiat
Authors: Panida Jitviriyavasin
Kannicha Khamjring
Pacharasiri Siriyom
Panida Jitviriyavasin
Keywords: Age detection
Face detection
Gender detection
Keras
Tensorflow
Issue Date: 2021
Publisher: Department of Computer Science, Srinakharinwirot University
Abstract: Currently, automatic age and gender predictions based on face detection draw a lot of attention due to their wide areas of applications. In this study, we try to build a system that consists of age, gender and emotion prediction models. Based on deep convolutional neural network architecture, age and gender models are trained by public dataset with 14,000 data instances. After implementing the primary models using Keras, we convert the model using TFLiteConverter, so that the model can be deployed as a mobile application. The performance of the three models are found as follow: using MAE as the evaluation index, the age model yields MAE of 0.1668; the gender model yields the accuracy of 0.95 and the emotion prediction model yield the accuracy of 0.62. We found that the causes of models inaccuracy included the images with some nonstandard poses, for example, skewed faces, distant faces, makeup on the faces, light, and shadow of the image, etc. By reducing these factors, the accuracy of the models can improve.
URI: https://ir.swu.ac.th/jspui/handle/123456789/22172
Appears in Collections:ComSci-Senior Projects

Files in This Item:
There are no files associated with this item.


Items in SWU repository are protected by copyright, with all rights reserved, unless otherwise indicated.