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

机译：基于分类系统的SURF描述符模板的面部表情自适应训练

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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.
机译：大多数面部表情识别（FER）系统都使用在短时间内创建的面部表情数据，并且该数据用于FER系统的学习/培训。有很多面部表情模式（即特定的表情可以用许多不同的模式表示），它们无法在短时间内生成并用作学习/训练数据。因此，为了长时间保持面部表情识别系统的高精度和鲁棒性，分类器应该随时间和空间自适应地发展。我们提出了一种具有增量学习能力的面部表情识别系统，因此可以学习可能在特征中生成的表情的所有可能模式。提取出感兴趣区域（面部）后，系统将提取加速鲁棒特征（SURF）。提出了一种基于SURF描述符模板的最近邻分类器进行分类。该分类器用作增量学习算法Learn ++的基础/弱分类器。在五个不同的数据库上进行了大量的实验，证明了所提出系统的增量学习能力。使用渐进式学习分类的实验证明了有希望的结果。

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