Automatic facial expression recognition using statistical-like moments
作者： Roberto D’Ambrosio , Giulio Iannello , Paolo Soda
DOI: 10.1007/978-3-642-24085-0_60
关键词:
摘要: Research in automatic facial expression recognition has permitted the development of systems discriminating between six prototypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, frontal video sequences. Achieving high rate often implies computational costs that are not compatible with real time applications on limited-resource platforms. In order to have as well efficiency, we propose an system using a set novel features inspired by statistical moments. Such descriptors, named statisticallike moments extract statistic from texture descriptors such local binary patterns. The approach been successfully tested second edition Cohn-Kanade database, showing advantage achieving performance comparable than methods based different
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