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首页> 外国专利> Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus

Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus

机译：弱假设生成设备和方法，学习设备和方法，检测设备和方法，面部表情学习设备和方法，面部表情识别设备和方法以及机器人设备

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摘要

A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.
机译：一种面部表情识别系统，该面部表情识别系统使用面部检测设备，当检测到表示检测对象的区域时，基于整体学习实现高效的学习和高速检测处理，并且对于图像中包含的面部位置的移动具有鲁棒性，并且能够进行高精度的表情提供了一种识别方法以及该系统的学习方法。当Adaboost学习面部检测设备要使用的数据时，进行处理以从所有弱假设中选择高性能弱假设，然后根据统计特征从这些高性能弱假设中生成新的弱假设，并选择一个弱假设。从这些弱假设中具有最高判别性能的假设被重复以依次产生弱假设，从而获得最终假设。在检测中，使用预先学习的中止阈值，每当一个弱假设输出判别结果时，就确定提供的数据是否可以明显地判断为非面部。如果可以这样判断，则中止处理。通过Adaboost技术从检测到的面部图像中选择预定的Gabor滤波器，并且仅学习由所选择的滤波器提取的特征量的支持向量，从而执行表情识别。

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