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.