Automatic Face Recognition via Local Directional Patterns



Automatic facial recognition has many potential applications in different areas of human
computer interaction. However, they are not yet fully realized due to the lack of an effective
facial feature descriptor. In this paper, we present a new appearance based feature descriptor,
the local directional pattern (LDP), to represent facial geometry and analyze its performance in
recognition. An LDP feature is obtained by computing the edge response values in 8 directions at
each pixel and encoding them into an 8 bit binary number using the relative strength of these
edge responses. The LDP descriptor, a distribution of LDP codes within an image or image
patch, is used to describe each image. Two well-known machine learning methods, template
matching and support vector machine, are used for classification using the ORL female facial
expression databases. Better classification accuracy shows the superiority of LDP descriptor
against other appearance-based feature descriptors. Entropy + LDP + SVM is as an improved
algorithm for facial recognition than previous presented methods that improves recognition rate
by features extraction of images. Test results showed that Entropy + LDP + SVM, method
presented in this paper, is fast and efficient. Innovation proposed in this paper is the use of
entropy operator before applying LDP feature extraction method. The test results showed that the
application of this method on ORL database images causes 3 percent increases in comparison
with not using entropy operator.


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