| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
This research proposed a personal identification method using body motion data and size data obtained from the Kinect sensor. This method is used in the night. From the motion data, personal identification is performed in an eigenspace smaller than the actual parameter space. In this case, the calculation efficiency is further improved by using several 2D eigenplanes. This method is called a higher order eigenspace method. On the other hand, from the size data, the nearest neighbor method based on Euclidean distance has the highest identification rate. In addition, the identification rate is more improved by a strong classifier to be determined by the minimum sum of ranks from the higher order eigenspace method and the nearest neighbor method.
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