  Facial expression recognition has many potential applications which has
attracted the attention of researchers in the last decade. Feature extraction
is one important step in expression analysis which contributes toward fast and
accurate expression recognition. This paper represents an approach of combining
the shape and appearance features to form a hybrid feature vector. We have
extracted Pyramid of Histogram of Gradients (PHOG) as shape descriptors and
Local Binary Patterns (LBP) as appearance features. The proposed framework
involves a novel approach of extracting hybrid features from active facial
patches. The active facial patches are located on the face regions which
undergo a major change during different expressions. After detection of facial
landmarks, the active patches are localized and hybrid features are calculated
from these patches. The use of small parts of face instead of the whole face
for extracting features reduces the computational cost and prevents the
over-fitting of the features for classification. By using linear discriminant
analysis, the dimensionality of the feature is reduced which is further
classified by using the support vector machine (SVM). The experimental results
on two publicly available databases show promising accuracy in recognizing all
expression classes.
