This paper proposes a method for facial expression recognition. Facial feature vectors are generated from keypoint descriptors using Speeded-Up Robust Features. Each facial feature vector is then normalized and next the probability density function descriptor is generated. The distance between two probability density function descriptors is calculated using Kullback Leibler divergence. Mathematical equation is employed to select certain practicable probability density function descriptors for each grid, which are used as the initial classification. Subsequently, the corresponding weight of the class for each grid is determined using a weighted majority voting classifier. The class with the largest weight is output as the recognition result. The proposed method shows excellent performance when applied to the Japanese Female Facial Expression database.
|Number of pages||14|
|Journal||Electronic Letters on Computer Vision and Image Analysis|
|Publication status||Published - 2012 Dec 1|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition