3D face recognition in the Fourier domain using deformed circular curves

Abstract

One of the most significant problems in image and vision applications is the efficient representation of a target image containing a large amount of data with high complexity. The ability to analyze high dimensional signals in a lower dimension without losing their information, has been crucial in the field of image processing. This paper proposes an approach to 3D face recognition using dimensionality reduction based on deformed circular curves, on the shortest geodesic distances, and on the properties of the Fourier Transform. Measured geodesic distances information generates a matrix whose entities are geodesic distances between the reference point and an arbitrary point on a 3D object, and an onedimensional vector is generated by reshaping the matrix without losing the original properties of the target object. Following the property of the Fourier Transform, symmetry of the magnitude response, the original signal can be analyzed in the lower dimensional space without loss of inherent characteristics. This paper mainly deal with the efficient representation and recognition algorithm using deformed circular curves and the simulation shows promising result for recognition of geometric face information.

3D Human Facial Traits’ Analysis for Ethnicity Recognition Using Deep Learning

“…The scans have been registered, deformed, and compared in this work, in addition to calculating the covariance, the PCA components, and analyzing the face symmetry. Lee and Krim [28] measured the shortest distances from a reference to points lying on a specific surrounding curve using Geodesic. The author then measured the similarity after extracting the deformed curves and used the Fourier transform to reduce the dimensionality of the feature space.…”