Automatic Face Recognition via Local Directional Patterns

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

Maryam Moghaddam ¹ , Saeed Meshgini ²

1 -
2 -

Received: 2016-02-22 Accepted : 2016-02-22 Published : 2016-12-01

Keywords: Entropy, Support vector machine, Facial recognition, Local Directional Pattern, Texture Image, Features extraction,

Abstract :

Automatic facial recognition has many potential applications in different areas of humancomputer interaction. However, they are not yet fully realized due to the lack of an effectivefacial 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 inrecognition. An LDP feature is obtained by computing the edge response values in 8 directions ateach pixel and encoding them into an 8 bit binary number using the relative strength of theseedge responses. The LDP descriptor, a distribution of LDP codes within an image or imagepatch, is used to describe each image. Two well-known machine learning methods, templatematching and support vector machine, are used for classification using the ORL female facialexpression databases. Better classification accuracy shows the superiority of LDP descriptoragainst other appearance-based feature descriptors. Entropy + LDP + SVM is as an improvedalgorithm for facial recognition than previous presented methods that improves recognition rateby features extraction of images. Test results showed that Entropy + LDP + SVM, methodpresented in this paper, is fast and efficient. Innovation proposed in this paper is the use ofentropy operator before applying LDP feature extraction method. The test results showed that theapplication of this method on ORL database images causes 3 percent increases in comparisonwith not using entropy operator.

References:

[1] A. Kar, D. Bhattacharjee, D. K. Basu, M. Nasipuri, M. Kundu, (2011). “An Adaptive Block-based Integrated LDP, GLCM, and Morphological Features for Face Recognition”, International Journal of Research and Reviews in Computer Science, Vol. 2, No. 5, pp. 1225-1211.
[2] Ambika Ramchandra, Ravindra Kumar, (2013).
Overview of Face Recognition System
Challenges, INTERNATIONAL JOURNAL OF
SCIENTIFIC & TECHNOLOGY RESEARCH
VOLUME 2, ISSUE 8.
[3] R. Verma and M.Y. Dabbagh, (2012). “Fast Facial Expression Recognition based on Local Binary Patterns”, 62th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4.
[4] Krishnakant Kishore, Vinit Kumar Gunjan, Gautam Bommagani , poorva Paidipelli, Pooja (2013). “Design, Implementation and Evaluation of an Algorithm for Face Recognition Based on
Modified Local Directional PatternFace
Recognition using LDP5 “International Journal of
Engineering Research Technology (IJERT)Vol. 2
Issue 11.
[5] Taskeed Jabid, Md. Hasanul Kabir, and Oksam Chae (2010). “Robust Facial Expression Recognition Based on Local Directional Pattern”, vol. 32, no., pp. 784-794, 5