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Sidra Batool Kazmi Qurat-ul-Ain M. Arfan Jaffar 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(3):369-379
A human face does not play its role in the identification of an individual but also communicates useful information about
a person’s emotional state at a particular time. No wonder automatic face expression recognition has become an area of great
interest within the computer science, psychology, medicine, and human–computer interaction research communities. Various feature
extraction techniques based on statistical to geometrical data have been used for recognition of expressions from static images
as well as real-time videos. In this paper, we present a method for automatic recognition of facial expressions from face
images by providing discrete wavelet transform features to a bank of seven parallel support vector machines (SVMs). Each SVM
is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification
is achieved by combining multiple SVMs performing binary classification using one-against-all approach. The outputs of all
SVMs are combined using a maximum function. The classification efficiency is tested on static images from the publicly available
Japanese Female Facial Expression database. The experiments using the proposed method demonstrate promising results. 相似文献
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