Gesture recognition using depth images

Abstract

Methods, apparatuses, and articles associated with gesture recognition using depth images are disclosed herein. In various embodiments, an apparatus may include a face detection engine configured to determine whether a face is present in one or more gray images of respective image frames generated by a depth camera, and a hand tracking engine configured to track a hand in one or more depth images generated by the depth camera. The apparatus may further include a feature extraction and gesture inference engine configured to extract features based on results of the tracking by the hand tracking engine, and infer a hand gesture based at least in part on the extracted features. Other embodiments may also be disclosed and claimed.

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         13 . A method comprising:
 tracking, by a computing apparatus, a hand in selected respective regions of one or more depth images generated by a depth camera, wherein the selected respective regions are size-wise smaller than the respective one or more depth images; and   inferring a hand gesture, by the computing device, based at least in part on a result of the tracking;   wherein tracking comprises determining location measures of the hand for the depth images.   
     
     
         14 . The method of  claim 13 , wherein determining location measures of the hand for the depth images comprises determining a pair of (x, y) coordinates for a center of the hand, using mean-shift filtering that uses gradients of probabilistic density. 
     
     
         15 . The method of  claim 13 , wherein inferring comprises extracting one or more features for the selected respective regions, based at least in part on a result of the tracking, and inferring a hand gesture based at least in part on the extracted one or more features. 
     
     
         16 . The method of  claim 15 , wherein extracting one or more features comprises extracting one or more of an eccentricity measure, a compactness measure, an orientation measure, a rectangularity measure, a horizontal center measure, a vertical center measure, a minimum bounding box angle measure, a minimum bounding box width-to-height ratio measure, a difference between left-and-right measure, or a difference between up-and-down measure. 
     
     
         17 . The method of  claim 13 , wherein inferring a gesture comprises inferring one of an open gesture, a fist gesture, a thumb up gesture, a thumb down gesture, a thumb left gesture or a thumb right gesture. 
     
     
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         19 . A method comprising:
 extracting, by a computing apparatus, one or more features from respective regions of depth images of image frames generated by a depth camera; and   inferring a gesture, by the computing apparatus, based at least in part on the one or more features extracted from the depth images;   wherein extracting one or more features comprises extracting one or more of an eccentricity measure, a compactness measure, an orientation measure, a rectangularity measure, a horizontal center measure, a vertical center measure, a minimum bounding box angle measure, a minimum bounding box width-to-height ratio measure, a difference between left-and-right measure, or a difference between up-and-down measure.   
     
     
         20 . The method of  claim 19 , wherein extracting one or more features from respective regions of depth images comprises extracting one or more features from respective regions of depth images denoted as containing a hand. 
     
     
         21 . The method of  claim 19  wherein inferring a gesture comprises inferring one of an open gesture, a fist gesture, a thumb up gesture, a thumb down gesture, a thumb left gesture or a thumb right gesture. 
     
     
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         23 . A computer-readable non-transitory storage medium, comprising
 a plurality of programming instructions stored in the storage medium, and configured to cause an apparatus, in response to execution of the programming instructions by the apparatus, to perform operations including:   tracking a hand in selected respective regions of one or more depth images generated by a depth camera, wherein the selected respective regions are size-wise smaller than the respective one or more depth images; and   inferring a hand gesture, based at least in part on a result of the tracking;   wherein tracking comprises determining location measures of the hand for the depth images.   
     
     
         24 . The storage medium of  claim 23 , wherein determining location measures of the hand for the depth images comprises determining a pair of (x, y) coordinates for a center of the hand, using mean-shift filtering that uses gradients of probabilistic density. 
     
     
         25 . The storage medium of  claim 23 , wherein inferring comprises extracting one or more features for the selected respective regions, based at least in part on a result of the tracking, and inferring a hand gesture based at least in part on the extracted one or more features. 
     
     
         26 . The storage medium of  claim 25 , wherein extracting one or more features comprises extracting one or more of an eccentricity measure, a compactness measure, an orientation measure, a rectangularity measure, a horizontal center measure, a vertical center measure, a minimum bounding box angle measure, a minimum bounding box width-to-height ratio measure, a difference between left-and-right measure, or a difference between up-and-down measure. 
     
     
         27 . The storage medium of  claim 23 , wherein inferring a gesture comprises inferring one of an open gesture, a fist gesture, a thumb up gesture, a thumb down gesture, a thumb left gesture or a thumb right gesture. 
     
     
         28 . The storage medium of  claim 23 , wherein the operations further comprise determining whether a face is present in the one or more depth images' corresponding one or more gray images of respective image frames generated by a depth camera. 
     
     
         29 . The storage medium of  claim 28 , wherein determining whether a face is present comprises analyzing the one or more gray images using a Haar-Cascade model. 
     
     
         30 . The storage medium of  claim 28 , wherein the operations further comprise determining a measure of a distance between the face and the camera, using the one or more depth images. 
     
     
         31 . An apparatus, comprising:
 a tracking engine to track a hand in selected respective regions of one or more depth images generated by a depth camera, wherein the selected respective regions are size-wise smaller than the respective one or more depth images; and   an inference engine coupled with the tracking engine to infer a hand gesture based at least in part on a result of the tracking;   wherein to track a hand comprises to determine location measures of the hand for the depth images.   
     
     
         32 . The apparatus of  claim 31 , wherein to determine location measures of the hand for the depth images comprises to determine a pair of (x, y) coordinates for a center of the hand, using mean-shift filtering that uses gradients of probabilistic density. 
     
     
         33 . The apparatus of  claim 31 , wherein to infer comprises to extract one or more features for the selected respective regions, based at least in part on a result of the tracking, and to infer a hand gesture based at least in part on the extracted one or more features. 
     
     
         34 . The apparatus of  claim 33 , wherein to extract one or more features comprises to extract one or more of an eccentricity measure, a compactness measure, an orientation measure, a rectangularity measure, a horizontal center measure, a vertical center measure, a minimum bounding box angle measure, a minimum bounding box width-to-height ratio measure, a difference between left-and-right measure, or a difference between up-and-down measure. 
     
     
         35 . The apparatus of  claim 31 , wherein to infer a gesture comprises to infer one of an open gesture, a fist gesture, a thumb up gesture, a thumb down gesture, a thumb left gesture or a thumb right gesture. 
     
     
         36 . An apparatus comprising:
 an extraction engine to extract one or more features from respective regions of depth images of image frames generated by a depth camera; and   an inference engine coupled with the extraction engine to infer a gesture, based at least in part on the one or more features extracted from the depth images;   wherein to extract one or more features comprises to extract one or more of an eccentricity measure, a compactness measure, an orientation measure, a rectangularity measure, a horizontal center measure, a vertical center measure, a minimum bounding box angle measure, a minimum bounding box width-to-height ratio measure, a difference between left-and-right measure, or a difference between up-and-down measure.   
     
     
         37 . The apparatus of  claim 36 , wherein to extract one or more features from respective regions of depth images comprises to extract one or more features from respective regions of depth images denoted as containing a hand. 
     
     
         38 . The apparatus of  claim 36  wherein to infer a gesture comprises to infer one of an open gesture, a fist gesture, a thumb up gesture, a thumb down gesture, a thumb left gesture or a thumb right gesture.