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Kawaguchi et al., 2019 - Google Patents

Basic investigation of sign language motion classification by feature extraction using pre-trained network models

Kawaguchi et al., 2019

Document ID
7653900107122112644
Author
Kawaguchi K
Nishimura H
Wang Z
Tanaka H
Ohta E
Publication year
Publication venue
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

External Links

Snippet

This paper presents a method of classifying sign language motion using the feature elements extracted by using pre-trained networks. Good results for the image recognition were obtained using this approach. Sign language motions are diverse and complex, so it is …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

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    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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    • G06K9/62—Methods or arrangements for recognition using electronic means
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    • G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
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