A Comparative Study of Face Authentication using Extreme Learning Machine, Euclidean and Mahalanobis Distance Classification Methods
Abstract
Face recognition can be used for both verification and
identification. Today face recognition technology is being used to
combat passport fraud, support law enforcement, identify missing
children, and minimize benefit or identity fraud. The two main steps
in a face recognition system are: (i) to define an effective
representation of the face images, which includes sufficient
information of the face for future classification, (ii) to classify a new
face image with the chosen representation. In this paper, Extreme
Learning Machine method for face recognition is proposed and
compared with Euclidean and Mahalanobis distance methods for
better face recognition rate. The Mahalanobis distance is
a metric which is better adapted than the usual Euclidean distance to
settings involving non spherically symmetric distribution, where as
extreme learning machine (ELM) is an efficient learning algorithm
for generalized single hidden layer feed forward networks (SLFNs),
which performs well in classification applications. This will further
enhance the quality of facial image authentication. Various
experiments are done for 400 samples from ORL database for the
three methods and the results are analyzed.
Keywords
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