World Academy of Science, Engineering and Technology
Published March 2, 2015 | Version 10000915
Journal article Open

Local Spectrum Feature Extraction for Face Recognition

Description

This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.

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