Feature learning based on SAE-PCA network for human gesture recognition in RGBD images

Neurocomputing

Volumn 151, Issue P2, 2015, Pages 565-573

  Li, Shao Zi ^a   Yu, Bin ^a,b   Wu, Wei ^a   Su, Song Zhi ^a   Ji, Rong Rong ^a  

^a XIAMEN UNIVERSITY   (China)

^b GUIZHOU NORMAL UNIVERSITY   (China)

Author keywords

American sign language recognition; Auto encoder; Convolutional neural networks; Deep learning

Indexed keywords

CHANNEL CODING; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; DEEP NEURAL NETWORKS; LEARNING SYSTEMS; PALMPRINT RECOGNITION; SIGNAL ENCODING;

AMERICAN SIGN LANGUAGE; AUTO ENCODERS; FEATURE REPRESENTATION; GESTURE RECOGNITION SYSTEM; HUMAN GESTURE RECOGNITION; MICROSOFT KINECT; PRINCIPLE COMPONENT ANALYSIS; RESEARCH INTERESTS;

GESTURE RECOGNITION;

AMERICAN; ARTICLE; ARTIFICIAL NEURAL NETWORK; COLOR; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; HUMAN; HUMAN GESTURE RECOGNITION; INFORMATION PROCESSING DEVICE; INTERMETHOD COMPARISON; MACHINE LEARNING; PRINCIPAL COMPONENT ANALYSIS; SENSOR; SIGN LANGUAGE; SPARSE AUTO ENCODER; VISION;

EID: 84919650221     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.06.086     Document Type: Article

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