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
First Claim
1. A weak hypothesis generation apparatus for generating a weak hypothesis for estimating whether provided data is a detection target or not by using a data set including plural learning samples, each of which has been labeled as a detection target or non-detection target, the weak hypothesis generation apparatus comprising:
- a selection unit for selecting a part of plural hypotheses and selecting one or plural weak hypotheses having higher estimation performance than others with respect to the data set of the selected part of the hypotheses, as high-performance weak hypotheses;
a new weak hypothesis generation unit for generating one or more new weak hypotheses formed by adding a predetermined modification to the high-performance weak hypotheses, as new weak hypotheses; and
a weak hypothesis selection unit for selecting one weak hypothesis having the highest estimation performance with respect to the data set, from the high-performance weak hypotheses and the new weak hypotheses.
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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.
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Citations
50 Claims
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1. A weak hypothesis generation apparatus for generating a weak hypothesis for estimating whether provided data is a detection target or not by using a data set including plural learning samples, each of which has been labeled as a detection target or non-detection target, the weak hypothesis generation apparatus comprising:
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a selection unit for selecting a part of plural hypotheses and selecting one or plural weak hypotheses having higher estimation performance than others with respect to the data set of the selected part of the hypotheses, as high-performance weak hypotheses;
a new weak hypothesis generation unit for generating one or more new weak hypotheses formed by adding a predetermined modification to the high-performance weak hypotheses, as new weak hypotheses; and
a weak hypothesis selection unit for selecting one weak hypothesis having the highest estimation performance with respect to the data set, from the high-performance weak hypotheses and the new weak hypotheses. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A weak hypothesis generation method for generating a weak hypothesis for estimating whether provided data is a detection target or not by using a data set including plural learning samples, each of which has been labeled as a detection target or non-detection target, the weak hypothesis generation method comprising:
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a selection step of selecting a part of plural hypotheses and selecting one or plural weak hypotheses having higher estimation performance than others with respect to the data set of the selected part of the hypotheses, as high-performance weak hypotheses;
a new weak hypothesis generation step of generating one or more new weak hypotheses formed by adding a predetermined modification to the high-performance weak hypotheses, as new weak hypotheses; and
a weak hypothesis selection step of selecting one weak hypothesis having the highest estimation performance with respect to the data set, from the high-performance weak hypotheses and the new weak hypotheses.
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14. A weak hypothesis generation apparatus for generating a weak hypothesis for estimating whether provided data is a detection target or not by using a data set including plural learning samples each of which has been labeled as a detection target or non-detection target, the weak hypothesis generation apparatus comprising:
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a selection unit for selecting a part of plural weak hypotheses;
a new weak hypothesis generation unit for generating one or more new weak hypotheses formed by adding a predetermined modification to the part of the weak hypotheses selected by the selection unit, as new weak hypotheses; and
a weak hypothesis selection unit for selecting one weak hypothesis having the highest estimation performance with respect to the data set, from the part of the weak hypotheses selected by the selection unit and the new weak hypotheses.
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15. A weak hypothesis generation method for generating a weak hypothesis for estimating whether provided data is a detection target or not by using a data set including plural learning samples each of which has been labeled as a detection target or non-detection target, the weak hypothesis generation method comprising:
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a selection step of selecting a part of plural weak hypotheses;
a new weak hypothesis generation step of generating one or more new weak hypotheses formed by adding a predetermined modification to the part of the weak hypotheses selected at the selection step, as new weak hypotheses; and
a weak hypothesis selection step of selecting one weak hypothesis having the highest estimation performance with respect to the data set, from the part of the weak hypotheses selected at the selection step and the new weak hypotheses.
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16. A learning apparatus for learning data to be used by a detection apparatus, the detection apparatus being adapted for judging whether provided data is a detection target or not by using a data set including plural learning samples each of which has data weighting set thereon and has been labeled as a detection target or non-detection target, the learning apparatus comprising:
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a weak hypothesis generation unit for generating a weak hypothesis for estimating whether provided data is a detection target or not;
a reliability calculation unit for calculating reliability of the weak hypothesis on the basis of the result of estimation of the weak hypothesis generated by the weak hypothesis generation unit with respect to the data set; and
a data weighting update unit for updating the data weighting in such a manner that the data weighting of a learning sample on which estimation by the weak hypothesis generated by the weak hypothesis generation unit is incorrect becomes relatively larger than the data weighting of a learning sample on which estimation is correct;
wherein the weak hypothesis generation unit a selection unit for selecting a part of plural weak hypotheses and selecting one or plural weak hypotheses having higher estimation performance with respect to the data set from the selected part of the weak hypotheses, as high-performance weak hypotheses, a new weak hypothesis generation unit for generating one or more new weak hypotheses by adding a predetermined modification to the high-performance weak hypotheses, and a weak hypothesis selection unit for selecting one weak hypothesis having the highest estimation performance with respect to the data set from the high-performance weak hypotheses and the new weak hypotheses, the weak hypothesis generation unit repeating processing to generate a weak hypothesis every time the data weighting is updated by the data weighting update unit.
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17. A learning apparatus for learning data to be used by a detection apparatus, the detection apparatus being adapted for judging whether provided data is a detection target or not by using a data set including plural learning samples each of which has been labeled as a detection target or non-detection target, the learning apparatus comprising:
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a weak hypothesis selection unit for repeating processing to select one weak hypothesis from plural weak hypotheses for estimating whether provided data is a detection target or not;
a reliability calculation unit for, every time a weak hypothesis is selected by the weak hypothesis selection unit, calculating reliability of the weak hypothesis on the basis of the result of estimation of the selected weak hypothesis with respect to the data set; and
a threshold value learning unit for calculating and adding the product of the result of estimation of the weak hypothesis with respect to the data set and the reliability of the weak hypothesis every time a weak hypothesis is selected by the weak hypothesis selection unit, and learning an abort threshold value for aborting the processing by the detection apparatus to judge whether the provided data is a detection target or not on the basis of the result of the addition. - View Dependent Claims (18, 19, 20, 21, 22)
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23. A learning method for learning data to be used by a detection apparatus, the detection apparatus being adapted for judging whether provided data is a detection target or not by using a data set including plural learning samples each of which has been labeled as a detection target or non-detection target, the learning method comprising:
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a weak hypothesis selection step of repeating processing to select one weak hypothesis from plural weak hypotheses for estimating whether provided data is a detection target or not;
a reliability calculation step of, every time a weak hypothesis is selected at the weak hypothesis selection step, calculating reliability of the weak hypothesis on the basis of the result of estimation of the selected weak hypothesis with respect to the data set; and
a threshold value learning step of calculating and adding the product of the result of estimation of the weak hypothesis with respect to the data set and the reliability of the weak hypothesis every time a weak hypothesis is selected at the weak hypothesis selection step, and learning an abort threshold value for aborting the processing by the detection apparatus to judge whether the provided data is a detection target or not on the basis of the result of the addition.
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24. A detection apparatus for detecting a detection target by discriminating whether provided data is a detection target or not, the detection apparatus comprising:
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an estimation result output unit including plural weak hypotheses; and
a discrimination unit for discriminating whether the provided data is a detection target or not on the basis of the result of output of the estimation result output unit;
wherein the estimation result output unit estimates and outputs whether the provided data is a detection target or not for each weak hypothesis on the basis of a feature quantity that has been learned in advance, and the discrimination unit has an abort unit for adding the product of the result of estimation of a weak hypothesis and reliability that has been learned in advance on the basis of estimation performance of the weak hypothesis, every time one hypothesis outputs the result of estimation, and deciding whether or not to abort processing by the estimation result output unit on the basis of the result of the addition. - View Dependent Claims (25, 26, 27, 28, 29, 30)
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31. A detection method for detecting a detection target by discriminating whether provided data is a detection target or not, the detection method comprising:
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an estimation result output step of estimating and outputting whether the provided data is a detection target or not by each of plural weak hypotheses on the basis of a feature quantity that has been learned in advance; and
a discrimination step of discriminating whether the provided data is a detection target or not on the basis of the result of output from the estimation result output step;
wherein the discrimination step includes an abort step of adding the product of the result of estimation of a weak hypothesis and reliability that has been learned on the basis of estimation performance of the weak hypothesis, every time one hypothesis outputs the result of estimation, and deciding whether or not to abort processing by the plural weak hypotheses on the basis of the result of the addition.
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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. An autonomously acting robot apparatus comprising:
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an image pickup unit for picking up an image of its surroundings;
a cut-out unit for cutting out a window image of an arbitrary size from the image picked up by the image pickup unit; and
a detection apparatus for detecting whether the window image is an image representing a detection target or not, wherein the detection apparatus has an estimation result output unit including plural weak hypotheses, and a discrimination unit for discriminating whether the window image is an image representing a detection target or not on the basis of the result of estimation outputted from the estimation result output unit, and wherein the estimation result output unit estimates and outputs whether the provided data is a detection target or not for each weak hypothesis on the basis of a feature quantity that has been learned in advance, and the discrimination unit has an abort unit for adding the product of the result of estimation of a weak hypothesis and reliability learned on the basis of estimation performance of the weak hypothesis every time one weak hypothesis outputs the result of estimation, and deciding whether or not to abort processing by the estimation result output unit on the basis of the result of addition.
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50. An autonomously acting robot apparatus comprising:
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an image pickup unit for picking up an image of its surroundings;
a face image detection apparatus for detecting a predetermined area as a face image from the image picked up by the image pickup unit; and
a facial expression recognition apparatus for recognizing an expression of the face image, wherein the facial expression recognition apparatus has a face feature extraction unit for filtering the face image detected by the face image detection apparatus 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.
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Specification