Enhanced gesture-based human-computer interaction through a Compressive Sensing reduction scheme of very large and efficient depth feature descriptors

Mantecón, Tomás; Mantecon, Ana; del-Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

doi:10.1109/avss.2015.7301804

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…To achieve the characterization of the hand gesture information in near-infrared imagery, some modifications have been made over the Depth Spatiograms of Quantized Patterns (DSQP) feature descriptor presented in [14], that is an evolution of the LBP algorithm [18]. There are two main differences between the original LBP and the DSQP descriptor.…”

Section: Dsqp-based Feature Descriptionmentioning

confidence: 99%

Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller

Mantecón

del-Blanco

Jaureguizar

et al. 2016

Lecture Notes in Computer Science

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Hand gestures are one of the main alternatives for Human-Computer Interaction. For this reason, a hand gesture recognition system using near-infrared imagery acquired by a Leap Motion sensor is proposed. The recognition system directly characterizes the hand gesture by computing a global image descriptor, called Depth Spatiograms of Quantized Patterns, without any hand segmentation stage. To deal with the high dimensionality of the image descriptor, a Compressive Sensing framework is applied, obtaining a manageable image feature vector that almost preserves the original information. Finally, the resulting reduced image descriptors are analyzed by a set of Support Vectors Machines to identify the performed gesture independently of the precise hand location in the image. Promising results have been achieved using a new hand-based near-infrared database.

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Section: Dsqp-based Feature Descriptionmentioning

confidence: 99%

Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller

Mantecón

del-Blanco

Jaureguizar

et al. 2016

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A hand gesture-based recognition system is presented in [6] with the aim of recognizing nger-spelling using the American Sign Language. The main novelty is the introduction of a Compressive Sensing step to reduce the dimension of a depth-based feature descriptor, called Depth Spatiograms of Quantized Patterns, which is very discriminative, but also too large for its practical application.…”

Section: Main Contributions Of the Thesismentioning

confidence: 99%

“…6 shows an example of a pixel which DLQP value is 134, so when that pixel is being evaluated, a weight which is inverse to the distance to the central pixel of each sub-block is added to the element 134 of each vector for every sub-block.…”

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confidence: 99%

“…6 shows samples of every hand pose of the database for both static and dynamic gestures performed by dierent subjects. In the rst row from left to right:…”

mentioning

confidence: 99%

“…6.1 shows the ROC curves for each binary classier corresponding to each subject of the LRRFaceD database, using the BAG-D3P feature descriptor. It can be observed that all the classiers have a ROC curve close to the optimal one.…”

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confidence: 99%

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