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An adaptive training based on classification system for patterns in facial expressions using SURF descriptor templates

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Abstract

Most facial expression recognition (FER) systems used facial expression data created during a short period of time and this data is used for learning/training of FER systems. There are many facial expression patterns (i.e. a particular expression can be represented in many different patterns) which cannot be generated and used as learning/training data in a short time. Therefore, in order to maintain its high accuracy and robustness for a long time of a facial expression recognition system, the classifier should be evolved adaptively over time and space. We proposed a facial expression recognition system that has the aptitude of incrementally learning and thus can learn all possible patterns of expressions that may be generated in feature. After extraction of region of interest (face), the system extracts Speeded-Up Robust Features (SURF). A novel SURF descriptor template based nearest neighbor classifier is proposed for classification. This classifier is used as base/weak classifier for incremental learning algorithm Learn++. A vast range of experimentation is performed on five different databases that demonstrate the incremental learning capability of the proposed system. The experiments using the incrementally learning classification demonstrate promising results.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan

    M. Sultan Zia & M. Arfan Jaffar

  2. COMSATS, Institute of Information Technology, Sahiwal, Pakistan

    M. Sultan Zia

  3. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    M. Arfan Jaffar

Authors
  1. M. Sultan Zia
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  2. M. Arfan Jaffar
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Correspondence to M. Arfan Jaffar.

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Zia, M.S., Jaffar, M.A. An adaptive training based on classification system for patterns in facial expressions using SURF descriptor templates. Multimed Tools Appl 74, 3881–3899 (2015). https://doi.org/10.1007/s11042-013-1803-3

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  • DOI: https://doi.org/10.1007/s11042-013-1803-3

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