Information processing apparatus and information processing method
First Claim
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1. An information processing apparatus, the apparatus comprising:
- a plurality of sensing means for sensing a plurality of types of data from a space in which a person exists;
detection means for detecting a plurality of kinds of feature amounts from the plurality of types of data sensed by said plurality of sensing means;
calculation means for calculating respective valid values of the plurality of kinds of feature amounts detected by said detection means;
selection means for selecting at least one feature which has a relatively high valid value from among the plurality of kinds of feature amounts, on the basis of the respective valid values calculated by said calculation means; and
identification means for identifying the person by using at least the selected one feature amount with the relatively high valid value,wherein said calculation means includes;
means for performing processing for obtaining normalized output level Vi of modules for detecting the feature amounts with respect to all classes i in a final layer of a hierarchical neural network;
means for calculating a sum total S=V1+V2+, . . . VN of output levels in all the classes; and
means for calculating a valid value Yi of the feature amount in class i by computing Yi=ki×
Vi/S where ki represents the appearance frequency in class i.
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Abstract
A plurality of kinds of feature amounts are collected from image information and voice information on a person existing in a space, valid values of the collected feature amounts are calculated, feature amounts to be used for personal recognition are determined in the collected feature amounts on the basis of the calculated valid values, and personal recognition is performed by using the determined feature amounts.
57 Citations
13 Claims
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1. An information processing apparatus, the apparatus comprising:
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a plurality of sensing means for sensing a plurality of types of data from a space in which a person exists; detection means for detecting a plurality of kinds of feature amounts from the plurality of types of data sensed by said plurality of sensing means; calculation means for calculating respective valid values of the plurality of kinds of feature amounts detected by said detection means; selection means for selecting at least one feature which has a relatively high valid value from among the plurality of kinds of feature amounts, on the basis of the respective valid values calculated by said calculation means; and identification means for identifying the person by using at least the selected one feature amount with the relatively high valid value, wherein said calculation means includes; means for performing processing for obtaining normalized output level Vi of modules for detecting the feature amounts with respect to all classes i in a final layer of a hierarchical neural network; means for calculating a sum total S=V1+V2+, . . . VN of output levels in all the classes; and means for calculating a valid value Yi of the feature amount in class i by computing Yi=ki×
Vi/S where ki represents the appearance frequency in class i. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An information processing method, the method comprising:
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performing by a processor the following steps; a plurality of sensing steps of sensing a plurality of types of data from a space in which a person exists; a detection step of detecting a plurality of kinds of feature amounts from the plurality of types of data sensed in the plurality of sensing steps; a calculation step of calculating respective valid values of the plurality of kinds of feature amounts detected in the detection step; a selection step of selecting at least one feature amount which has a relatively high valid value from among the plurality of kinds of feature amounts, on the basis of the respective valid values calculated in the calculation step; and an identification step of identifying the person by using at least the selected one feature amount with the relatively high valid value, wherein said calculation step includes; obtaining normalized output level Vi of modules for detecting the feature amounts with respect to all classes i in a final layer of a hierarchical neural network; calculating a sum total S=V1+V2+, . . . VN of output levels in all the classes; and calculating a valid value Yi of the feature amount in class i by computing Yi=ki×
Vi/S where ki represents the appearance frequency in class i. - View Dependent Claims (13)
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Specification