3D-Difference Theoretic Texture Features for Dynamic Face Recognition

We define a new set of spatio-temporal features called the 3D-Difference Theoretic Texture Features (3D-DTTF) for dynamic face recognition from videos. The Difference Theoretic Texture Features (DTTF) is a low-dimensional 2D scale-, rotation-and illumination-invariant texture descriptor set which reported high accuracies for texture recognition experiments in [6]. The 3D-DTTF extends the gray-level difference statistics along the Front (F), Front-Diagonal Horizontal (FDH) and Front-Diagonal Vertical (FDV) directions in addition to the existing horizontal, vertical and diagonal directions in the two-imensional DTTF. The new 3D features are affine-invariant similar to their 2D counterpart, a property useful for recognizing faces in a video irrespective of the change in facial expressions. Experimentation on the Cohn-Kanade facial expression video dataset yields higher accuracy for the proposed dynamic face recognition as compared to other methods.