The automatic recognition of facial expressions is critical to applications that are required to recognize human emotions, such as multimodal user interfaces. A novel framework for recognizing facial expressions is presented in this paper. First, distance-based features are introduced and are integrated to yield an improved discriminative power. Second, a bag of distances model is applied to comprehend training images and to construct codebooks automatically. Third, the combined distance-based features are transformed into mid-level features using the trained codebooks. Finally, a support vector machine (SVM) classifier for recognizing facial expressions can be trained. The results of this study show that the proposed approach outperforms the state-of-the-art methods regarding the recognition rate, using a CK+ dataset.
|Number of pages||18|
|Journal||Multimedia Tools and Applications|
|State||Published - 17 Sep 2014|
- Bag of distances
- Facial expression recognition
- Facial features