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
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Abstract
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.
41 Citations
49 Claims
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1-31. -31. (canceled)
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32. A facial expression learning apparatus for learning data to be used by a facial expression recognition apparatus, the facial expression recognition apparatus being adapted for recognizing an expression of a provided face image by using an expression learning data set including plural face images representing specific expressions as recognition targets and plural face images representing expressions different from the specific expressions,
the facial expression learning apparatus comprising an expression learning unit for learning data to be used by the facial expression recognition apparatus, the facial expression recognition apparatus identifying the face images representing the specific expressions from provided face images on the basis of a face feature extracted from the expression learning data set by using a Gabor filter.
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36. A facial expression learning method for learning data to be used by a facial expression recognition apparatus, the facial expression recognition apparatus being adapted for recognizing an expression of a provided face image by using an expression learning data set including plural face images representing specific expressions as recognition targets and plural face images representing expressions different from the specific expressions,
the facial expression learning method comprising an expression learning step of learning data to be used by the facial expression recognition apparatus, the facial expression recognition apparatus identifying the face images representing the specific expressions from provided face images on the basis of a face feature extracted from the expression learning data set by using a Gabor filter.
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40. A facial expression recognition apparatus comprising:
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a face feature extraction unit for filtering a provided face image by using a Gabor filter and thus extracting a face feature; and an expression recognition unit for recognizing an expression of the provided face image on the basis of the face feature. - View Dependent Claims (41, 42, 43, 44)
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45. A facial expression recognition method comprising:
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a face feature extraction step of filtering a provided face image by using a Gabor filter and thus extracting a face feature; and an expression recognition step of recognizing an expression of the provided face image on the basis of the face feature. - View Dependent Claims (46, 47, 48)
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49-50. -50. (canceled)
Specification