Robust Facial Expression Recognition Based on Local Directional Pattern

Automatic facial expression 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...

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Vydané v:ETRI journal Ročník 32; číslo 5; s. 784 - 794
Hlavní autori: Jabid, Taskeed, Kabir, Md. Hasanul, Chae, Oksam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 한국전자통신연구원 01.10.2010
Predmet:
facial expression recognition
features extraction
Image representation
local directional pattern
principal component analysis
support vector machine
전자/정보통신공학
ISSN:1225-6463, 2233-7326
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Shrnutí:Automatic facial expression 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 expression 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 expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well‐known machine learning methods, template matching and support vector machine, are used for classification using the Cohn‐Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance‐based feature descriptors.
Bibliografia:G704-001110.2010.32.5.005
ISSN:1225-6463
2233-7326
DOI:10.4218/etrij.10.1510.0132