A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition
Authors:
- 1 November 2009
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15224880,p. 3305-3308
Abstract
Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use pyramid histogram of oriented gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly available dataset demonstrated that the PHOG with a significantly shorter vector length could achieve as high a recognition rate as the Gabor features did. Furthermore, the feature selection conducted by an AdaBoost algorithm was not needed when using the PHOG features. To further improve the recognition performance, we combined these two feature extraction methods and achieved the best smile recognition rate, indicating a good value of the PHOG features for smile recognitions.Keywords
This publication has 10 references indexed in Scilit:
- Representing shape with a spatial pyramid kernel2007
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories2006
- Histograms of Oriented Gradients for Human Detection2005
- Dynamics of Facial Expression Extracted Automatically from Video2005
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Independent component analysis of gabor features for face recognitionIEEE Transactions on Neural Networks, 2003
- Comprehensive database for facial expression analysis2002
- A principal component analysis of facial expressionsVision Research, 2001
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997