Work

Biological landmark Vs quasi-landmarks for 3D face recognition and gender classification

Year: 2019

Type: article

Abstract: Face recognition and gender classification are vital topics in the field of computer graphic and pattern recognition. We utilized ideas from two growing ideas in computer vision, which are biological landmarks and quasi-landmarks (dense mesh) to propose a novel approach to compare their performance in face recognition and gender classification. The experimental work is conducted on FRRGv2 dataset and acquired 98% and 94% face recognition accuracies using the quasi and biological landmarks respectively. The gender classification accuracies are 92% for quasi-landmarks and 90% for biological landmarks.

Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

DOI: 10.11591/ijece.v9i5.pp4069-4076

Authors: Hawraa H. Abbas, Ammar A. Altameemi, Hameed R. Farhan

Institution: University of Kerbala

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2975617386

FWCI: 0.784

Cites: 42

Cited by: 11

Related to: 10