Details

Autor(en) / Beteiligte

• Kaili Zhao
• Wen-Sheng Chu
• De la Torre, Fernando
• Cohn, Jeffrey F.
• Honggang Zhang

Titel

Joint patch and multi-label learning for facial action unit detection

Ist Teil von

• 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015-06, Vol.2015, p.2207-2216

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2015

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The face is one of the most powerful channel of nonverbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). AUs, which may occur alone and in thousands of combinations, can describe nearly all-possible facial expressions. Most existing methods for automatic AU detection treat the problem using one-vs-all classifiers and fail to exploit dependencies among AU and facial features. We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. In four of five comparisons on three diverse datasets, CK+, GFT, and BP4D, JPML produced the highest average F1 scores in comparison with state-of-the art.