Please use this identifier to cite or link to this item:
Title: Face-based gender and ethnicity classification using golden ratio template
Authors: Fahimeh Saei Manesh
Keywords: DRNTU::Engineering
Issue Date: 2010
Abstract: There has been increasing interest of video surveillance systems, especially in places in need of high security, to reduce the crime rate. The most informative biometric modality for human identification is human face. The face carries several types of information of a person besides the identity such as his gender, age, ethnicity and emotion. Visual information such as gender, age and ethnicity, in other words soft biometric modals, play critical roles in human identification as well as face to face communication. Most gender and ethnicity recognition methods use the full face data assuming each face part has equal discriminant capability identification and recognition. In this research, we improve the gender and ethnicity recognition by employing the optimum decision making rule based on the confidence of 16 different face regions. Automatic facial patch extraction is done by the modified golden ratio template on the full face aligned with multiple base points to prevent the displacement of facial parts due to different facial part distances of the people in different genders and ethnicities.
Description: 90 p.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
9.59 MBAdobe PDFView/Open

Page view(s) 50

Updated on Dec 5, 2020

Download(s) 50

Updated on Dec 5, 2020

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.