Abstract
This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been shown recently that small local pixel pattern distributions can be highly discriminative. Motivated by such works, the proposed model employs higher-order statistics of local non-binarized pixel patterns for the image description. Hence, in addition to being remarkably simple, it requires neither any user specified quantization of the space (of pixel patterns) nor any heuristics for discarding low occupancy volumes of the space. This leads to a more expressive representation which, when combined with discriminative SVM classifier, consistently achieves state-of-the-art performance on challenging texture and facial analysis datasets outperforming contemporary methods (with similar powerful classifiers).
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Keywords
- Face Image
- Gaussian Mixture Model
- Local Binary Pattern
- Facial Expression Recognition
- Linear Support Vector Machine
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Sharma, G., ul Hussain, S., Jurie, F. (2012). Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33786-4_1
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