NDLI: Randomized decision forests for static and dynamic hand shape classification

Content Provider	IEEE Xplore Digital Library
Author	Keskin, C. Kirac, F. Kara, Y.E. Akarun, L.
Copyright Year	2012
Description	Author affiliation: Boğaziçi University, Computer Engineering Department, 34342, Istanbul, Turkey (Keskin, C.; Kirac, F.; Kara, Y.E.; Akarun, L.)
Abstract	This paper proposes a novel algorithm to perform hand shape classification using depth sensors, without relying on color or temporal information. Hence, the system is independent of lighting conditions and does not need a hand registration step. The proposed method uses randomized classification forests (RDF) to assign class labels to each pixel on a depth image, and the final class label is determined by voting. This method is shown to achieve 97.8% success rate on an American Sign Language (ASL) dataset consisting of 65k images collected from five subjects with a depth sensor. More experiments are conducted on a subset of the ChaLearn Gesture Dataset, consisting of a lexicon with static and dynamic hand shapes. The hands are found using motion cues and cropped using depth information, with a precision rate of 87.88% when there are multiple gestures, and 94.35% when there is a single gesture in the sample. The hand shape classification success rate is 94.74% on a small subset of nine gestures corresponding to a single lexicon. The success rate is 74.3% for the leave-one-subject-out scheme, and 67.14% when training is conducted on an external dataset consisting of the same gestures. The method runs on the CPU in real-time, and is capable of running on the GPU for further increase in speed.
Starting Page	31
Ending Page	36
File Size	527278
Page Count	6
File Format	PDF
ISBN	9781467316118
ISSN	21607508
e-ISBN	9781467316125
e-ISBN	9781467316101
DOI	10.1109/CVPRW.2012.6239183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image segmentation Accuracy Shape Vegetation Sensors Decision trees
Content Type	Text
Resource Type	Article

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