Su Chi, Yap and Syafiq Fauzi, Kamarulzaman (2019) Intelligent gender recognition system for classification of gender in Malaysian demographic. In: 5th International Conference on Electrical, Control and Computer Engineering (INECCE 2019) , 29-30 July 2019 , Swiss Garden Kuantan. pp. 1-12.. (Unpublished)
![[img]](http://umpir.ump.edu.my/style/images/fileicons/other.png) |
Pdf
14. Intelligent gender recognition system for classification.pdf Restricted to Repository staff only Download (2MB) | Request a copy |
|
|
Pdf
14.1 Intelligent gender recognition system for classification.pdf Download (85kB) | Preview |
Abstract
Intelligent gender recognition is the ability in identify whether a person is a male or female based on the past experiences by the features of face such as eyes, mouth, cheek that makes the significant contribution to the appearance. Detecting human gender is difficult but important for some purposes, especially where safety issues related to female gender in public amenities is concerned. There were criminal cases that happened around the world concerning female gender in the public bathroom. The objectives of this research is to identify the techniques for classifying the different face features of male and female, embed as a system and validify using photos within Malaysian demographic. The scope of the project is focused on the face features for gender classification in real time, utilizing deep learning based gender recognition and HAAR Cascade classifier using pre-trained caffe model in OpenCV library to detect the gender.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Gender Recognition; Gender Classification; Deep Learning |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 17 Dec 2019 03:37 |
| Last Modified: | 17 Dec 2019 03:37 |
| URI: | http://umpir.ump.edu.my/id/eprint/25998 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |