Recognizing facial expressions at low resolution

This paper focuses on recognizing facial expressions at low resolution. We introduce local binary patterns (LBP) as novel low-computation discriminative features for low-resolution facial expression recognition. Compared to Gabor wavelets, LBP features can be derived rapidly in a single scan of raw images, whilst still retaining enough facial information in a compact representation. Support vector machine (SVM) is adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade database demonstrate that the LBP features are effective and efficient for facial expression recognition, and crucially perform robustly and stably over a useful range of low resolutions. Our method yields promising performance when processing compressed low-resolution video sequences from the PETS 2003 dataset.