| Project/Area Number |
11555090
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
電子デバイス・機器工学
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| Research Institution | Tohoku University |
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Principal Investigator |
KOTANI Koji Tohoku University, Graduate School of Engineering, Associate Professor, 大学院・工学研究科, 助教授 (20250699)
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| Co-Investigator(Kenkyū-buntansha) |
OHMI Tadahiro Tohoku University, New Industry Creation Hatchery Center, Professor, 未来科学技術共同研究センター, 教授 (20016463)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥13,700,000 (Direct Cost: ¥13,700,000)
Fiscal Year 2000: ¥6,400,000 (Direct Cost: ¥6,400,000)
Fiscal Year 1999: ¥7,300,000 (Direct Cost: ¥7,300,000)
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| Keywords | Face Recognition / Codebook / Vector Quantization / Codebook Space Information / Facial Expression Recognition / Speaker Recognition / コードブックスペクトル |
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| Research Abstract |
Novel faco rccognition technology has been developed using "Vector-Quatization Codebook-Space Information Processing Algorithm." Detailed processing steps are as follows. Facial image is first divided into small size blocks (4X4) and differential intensity information is extracted by subtracting minimum intensity within the blocks. Then vector quantization is carried out using theoretically synthesized codebook and personal feature information is extracted by statistically analyzing referred frequenucies of each codebook vector. Finally, matching between the feature information and the database is carried out to identify the person. We have developed effective filtering procedure to eliminate noise and unwanted signal component and histogram standardization procedure to calibrate the size of faces. In addition, we have introduced "effective discrimination distanace" as a recognition measture in order to improve the recognition algorithm at higher success rate region. Finally, we have r
… More
ealized 100% recognition success rate for 44 person's 220 facial images.
We have applied the recognition algorithm into the facial expression recognition. 100% success rate has been realized in recognizing 3 facial expressions (anger, happiness, and normal) of the identical person. It is revealed by evaluating suitable filter size and image resolution that the signal components having a period of 13mm to 14 mm or longer at real space are very important for facial expression recognition.
We have also studied a speaker recognition technology for realizing highly accurate human recognition and identification. We have realized high performance speaker recognition algorithm, which utilizes feature extraction by cepstrum analysis and classification by vector quantization. Improvements on recognition speed and success rate were achieved by newly developed hierarchical matching method and pre-learning procedure. Finally, we have realized 97% recognition success rate at maximum for 58 person's 290 text-independent speeches. Less
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