Espressioni facciali

06 Maggio 2014
Riconoscimento automatico di espressioni facciali
Voti totali: 1
Riconoscimento automatico di espressioni facciali

Automatic Facial Expression Recognition by I. Martinelli


Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. In this work, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. The operator labels the pixels of an image by thresholding a neighborhood of each pixel with the center value and considering the results as a binary number, and the 256-bin histogram of the LBP labels computed over a region is used as a texture descriptor. The derived binary numbers (called Local Binary Patterns or LBP codes) codify local primitives including different types of curved edges, spots, flat areas, etc., so each LBP code can be regarded as a micro-texton. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition, and the best recognition performance is obtained by using Support Vector Machine classifiers.


Caifeng Shan, Shaogang Gong, and Peter W. McOwan. 2009. Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image Vision Comput. 27, 6 (May 2009), 803-816. DOI=10.1016/j.imavis.2008.08.005