Abstract:
A method of image acquisition and data pre-processing includes obtaining from a sensor an image of a subject making a movement. The sensor may be a depth camera. The method also includes selecting a plurality of features of interest from the image, sampling a plurality of depth values corresponding to the plurality of features of interest, projecting the plurality of features of interest onto a model utilizing the plurality of depth values, and constraining the projecting of the plurality of features of interest onto the model utilizing a constraint system. The constraint system may comprise an inverse kinematics solver.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 61/153,229, entitled “METHOD AND SYSTEM FOR GESTURE RECOGNITION”, filed Feb. 17, 2009, and is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND INFORMATION 
     There are many techniques for interpreting the movements of a player or user of a computer system so that the player or user can communicate with the computer system through a natural and intuitive interface. There has been much recent interest in the application of these interfaces to the home entertainment and gaming market. Notable among these are, for example, Nintendo Wii&#39;s controllers and the Wii Fit&#39;s Balance Board. The Nintendo controllers rely on accelerometers and also calculate the position of a controller by triangulation. Alternatively, many human-machine interface techniques rely on different types of cameras. An early example of a camera-based interface system is Sony&#39;s Eyetoy system, which uses a conventional color camera to detect rough movements and classify them as user-performed gestures. 
     In the context of a computer video game, there are several important considerations to take into account when designing the gesture recognition system, and their relative importance depends on how the gesture recognition system is used within the game. One use of the gesture recognition system is to allow for user feedback, as, once a particular gesture is recognized, pre-recorded animation sequences can be played to show the user what the system understands he did. A second use of the gesture recognition system is for scoring, as a gameplay mechanism, e.g., to add to the score, and to allow the player to advance to different levels. Thus, the way in which the gesture recognition system is used in the game places different constraints on the design of the system. As one example, if the system is used to provide the user with feedback as to the movements he performed, it is important to minimize the delay between the user&#39;s performance of the gesture and the system&#39;s recognition of same gesture. The sensitivity to the system delay is not as important if the gesture recognition system is being used in order to compute the player&#39;s score. 
     U.S. Pat. No. 7,340,077 describes a gesture recognition system that obtains position information indicating depth for a plurality of discrete regions on a body part of a person and then classifies the gesture using this information. According to the patent, there is an explicit start time which designates when to begin storing the discrete regions and also an explicit end time, which indicates that the user has completed the gesture. After explicitly identifying the start and end times, the comparison to the gesture library is performed. Consequently, an inherent lag is introduced by this method. In addition, the data collection is done directly on the depth data. That is, data points can only be sampled from depth data corresponding to “1” values on the binary mask. There are some limitations that result from the sampling of the data points from the depth data. Firstly, the depth data itself is typically noisy, and this can deleteriously affect the quality of the sampled values. Secondly, this method of sampling data points from the depth data is necessarily restricted to the field of view of the camera. 
     Summary The present invention relates to recognizing the gestures and movements performed by players in front of depth cameras, and, in one embodiment, the use of these gestures in order to drive gameplay in a computer video game. The following summary of the invention begins with several terms defined below. 
     Gesture Recognition System. A gesture recognition system is a system that recognizes and identifies pre-determined movements performed by a user in front of an input device, for example. Examples include interpreting data from a camera to recognize that a user has closed his hand, or interpreting the data to recognize a forward punch with the left hand. 
     Depth Sensors. The present invention may perform gesture recognition using data from depth sensors, which may be cameras that generate 3D data. There are several different types of depth sensors. Among these are cameras that rely on the time-of-flight principle, or on structured light technology, as well as stereoscopic cameras. These cameras may generate an image with a fixed resolution of pixels, where each pixel has an integer value, and these values correspond to the distance of the object projected onto that region of the image by the camera. In addition to this depth data, the depth cameras may also generate color data, in the same way that conventional color cameras do, and this data can be combined with the depth data for use in processing. Multiple frames of image depth data can be acquired by the camera. 
     Binary Mask. Using the depth data, it is also trivial to create a binary mask, which is an image of the same resolution as the original image, but all pixels have integer values corresponding to either 0 or 1. Typically, all pixels have a threshold and receive a value of 0 in the binary mask if the pixel value is below the threshold, and 1 if the pixel value is above the threshold. For example, in the case of a player standing in front of the depth camera, the binary mask is generated (and thus the threshold computed) so that pixels corresponding to the player&#39;s body are 1, and all other pixels are 0. Effectively then, the binary mask is the silhouette of the user, as captured by the camera. 
     Articulated Figure. An articulated figure is a collection of joints connected to each other in some fixed way and constrained to move in certain ways, e.g., a human skeleton. 
     Inverse Kinematics Solver. An Inverse Kinematics (IK) Solver may be used in the present invention. Given a desired configuration of an articulated figure (e.g. the positions of certain joints) the Inverse Kinematics Solver computes the angles between the given joints and other joints in the figure that yield the given locations of the selected joints. For example, given the locations of the wrist and shoulder, an IK Solver can compute the angles of the shoulder and elbow joints that yield these wrist and shoulder locations, thereby also effectively computing the location of the elbow joint. 
     U.S. patent application Ser. No. 11/866,280, entitled “METHOD AND SYSTEM FOR GESTURE CLASSIFICATION”, describes a method and system for using gesture recognition to drive gameplay in games and is incorporated by reference in its entirety. Such a method and system may be utilized by the present invention, as described below. In one embodiment, the method described in U.S. patent application Ser. No. 11/866,280 is applicable to data generated from the IK Solver model. 
     Within a certain margin of error, the parts of the body can be identified from the data produced by a depth camera. After the positions of the various parts of the body are identified on the depth image, the depth values can be sampled from the image, so that the three-dimensional (3D) positions of each body part are obtained. (This step is referred to as the tracking module.) A gesture recognition system can then be trained and implemented on these 3D positions corresponding to the points on the user&#39;s body. 
     In the current invention, the 3D positions corresponding to the parts of the body may be mapped onto a model. In one embodiment, an Inverse Kinematics (IK) Solver is used to project the data points obtained from the depth image onto the possible configurations human joints can take. The IK Solver model essentially acts as a constraint, and the data is filtered so that it fits within the framework of the model of natural human movement. 
     There are several important advantages in using an IK Solver to filter the data from the tracking module. First, the IK Solver model effectively smoothes the data, thereby minimizing the effects of camera noise. Second, the data points obtained from the tracking module necessarily correspond to pixels of value “1” on the binary mask (that is, they fall on the silhouette of the user). There is no such restriction pertaining to the data obtained by the IK Solver. To give a specific example, the player may be standing close to the edge of the camera&#39;s field of view. In this case, when he reaches out to the side, the end of his arm will be out of the field of view of the camera. In spite of this, the IK Solver module should compute that the player&#39;s arm is reaching out of the field of view and return the location of his hand. Obviously, there is no way to do this using only the data from the tracking module. A third advantage in using the IK Solver model is in dealing with occlusions. For example, often, the player&#39;s hand will occlude the camera&#39;s view of his elbow. Consequently, no data corresponding to the elbow can be sampled from the depth image (since its location is unknown). Given the locations of the hand and shoulder, however, the IK Solver model is able to calculate the approximate position of the elbow as well. 
     An additional component of this invention is the gesture classification method. The method described in U.S. patent application Ser. No. 11/866,280 is a binary classifier as to whether a gesture has been performed or not. That is, the method yields a binary, “yes” or “no” indication as to whether the gesture was performed or not. A characteristic of the method described in U.S. patent application Ser. No. 11/866,280 is that it must wait until the gesture is completed before deciding whether any of the gestures in the gesture library were performed. An alternative way to classify gestures is included in the present invention. Rather than deciding binary (“yes” or “no”) if the gesture was performed or not, the method described in the present invention tracks a gesture being performed frame by frame, and indicates after every frame how close the gesture being performed is to a given gesture in the gesture library. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Examples of a gesture recognition system and method are illustrated in the figures. The examples and figures are illustrative rather than limiting. 
         FIG. 1  illustrates a block diagram  100 A of the overall architecture of one embodiment of the gesture recognition system 
         FIG. 2  depicts a flow diagram illustrating an exemplary process  200 A for obtaining data from the camera and processing the data to obtain feature positions, according to an embodiment of the disclosure. 
         FIG. 3A  depicts a flow diagram illustrating an exemplary process  300 A for constructing a gesture library, according to an embodiment of the disclosure. 
         FIG. 3B  depicts a flow chart illustrating an exemplary process  300 B of creating a library of gestures using motion capture equipment, according to an embodiment of the disclosure. 
         FIG. 3C  depicts a flow chart illustrating an exemplary process  300 C of creating a library of gestures using color and depth images, according to an embodiment of the disclosure. 
         FIG. 4A  depicts a flow diagram illustrating an exemplary process  400 A for using a binary gesture recognition technique to determine whether the gesture being searched for was performed or not performed, according to an embodiment of the disclosure. 
         FIG. 4B  depicts a flow diagram illustrating an exemplary process  400 B for illustrating a method of identifying a gesture from movements captured in a sequence of images, according to an embodiment of the disclosure. 
         FIG. 5  depicts a flow diagram illustrating an exemplary process  500 A of verifying whether the player is performing a particular gesture or not over a period of time (i.e., sequence of frames), and determining how accurately the player is performing the prompted gesture, according to an embodiment of the disclosure. 
         FIG. 6  is a block diagram  600  of one embodiment of the gesture classification system incorporated into an interactive program, according to an embodiment of the disclosure. 
         FIG. 7  is a block diagram  700  of one embodiment of the gesture classification system incorporated into an interactive program accessed by multiple players over a network, according to an embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a block diagram  100 A of the overall architecture of one embodiment of the gesture recognition system. Image Acquisition &amp; Data Pre-Processing Module  200  obtains multiple frames of the image depth data from the camera and processes it before feeding the processed data to three other modules, the Gesture Training Module  300 , the Binary Gesture Recognition Module  400 , and the Real-Time Gesture Recognition Module  500 . In one embodiment, Gesture Training Module  300  trains the gesture recognition algorithms by computing the most efficient way to characterize particular gestures from the data fed into the module. In one embodiment, Gesture Training Module  300  is run as an offline task. The characterization of the gestures from the data that is generated by Gesture Training Module  300  is sent to Binary Gesture Recognition Module  400  and Real-Time Gesture Recognition Module  500 . In addition, the data from Image Acquisition &amp; Data Pre-Processing Module  200  is also sent to Binary Gesture Recognition Module  400  and Real-Time Gesture Recognition Module  500 . 
       FIG. 2  depicts a flow diagram illustrating an exemplary process  200 A for obtaining image data from the camera and processing the data to obtain feature positions, according to an embodiment of the disclosure. The Image Acquisition &amp; Data Pre-Processing Module  200  of the system performs process  200 A. In one embodiment, the feature positions are the joint positions. The obtained feature positions are sent to the Modules  300 ,  400 , and  500 . 
     At block  210 , module  200  obtains two-dimensional image data from the camera. This data can be either depth data alone or depth data and color data. 
     At block  220 , module  200  processes the data from the camera. This may be only the depth image, or it could be the depth image in conjunction with color images. Image processing algorithms are used to identify, as accurately as possible, the points on the two-dimensional (2D) images obtained from the camera corresponding to the various features of the object being tracked. If a player&#39;s body is being tracked, these features may include the player&#39;s head, right and left shoulder joints, right and left elbow joints, right and left hands, torso, pelvis, right and left knee joints. After the locations of the features of interest are identified on the 2D images, the depth values can be sampled from the depth image, to obtain three-dimensional (3D) positions of each feature. In one embodiment, this corresponds to obtaining 3D positions (including depth from the depth image) of each of the joints of interest. 
     At block  230 , the 3D positions of the joints obtained at block  220  are projected onto a model of the object being tracked. There are several different types of models upon which the data can be mapped. In one embodiment, the model may be a geometric shape. For example, the model could be a simple geometric representation of a human hand, with a palm and five fingers. In one embodiment, the model is a representation of the human skeleton, which is constrained to manifest only configurations that are consistent with natural human movements, through the use of an Inverse Kinematics (IK) Solver, or another constraint system. In one embodiment, the IK Solver solves a system of equations that model the possible configurations of the joints of the human skeleton and effectively acts as a constraint to each joint&#39;s freedom of movement. 
     Constraining the joint positions obtained at block  220  to the model at block  230  serves several important functions. First, it filters noise from the camera and effectively smoothes the results. Second, certain of the player&#39;s limbs may be out of the field of view of the camera. In this case, the model of block  230  is able to calculate the approximate locations of joints that are not in the camera&#39;s view. Third, it fills in the positions of joints that can not be obtained from the camera&#39;s data. An IK Solver is able to compute the locations of some joints given those of other “adjacent” joints. For example, if the player&#39;s hand is stretched outward directly towards the camera, his elbow and possibly shoulder are likely occluded from view. In this case, it is not possible to obtain the 3D positions of these joints at block  220 . At block  230 , however, the 3D positions of these joints are obtained from the model of the human skeleton, which is able to calculate the positions of some joints, given the locations of other joints. 
     Some embodiments include an optional block  240 , wherein the location data of the features (or joints) is scaled to a standard skeleton, or standard model. This is commonly called “animation retargeting.” This block is useful, although not required, because the training data and testing data must reference the same coordinate system, even though typically the training data is collected from users with different body proportions than those on whom the testing data is collected. In order to better apply the trained gestures to users&#39; bodies that were not included in the training data, the tracking data may be appropriately scaled. 
     At block  250 , the data is collected from the standard model used for animation retargeting. In one embodiment, this corresponds to obtaining the 3D positions of the joints from the skeleton model. At block  260 , the data retrieved from the model is sent to Gesture Training Module  300  for training gesture classification algorithms, as well as the Gesture Recognition Modules  400  and  500 . 
       FIG. 3A  depicts a flow diagram illustrating an exemplary process  300 A for constructing a gesture library. The Gesture Training Module  300  of the system performs process  300 A. At block  310 , module  300  receives feature data generated by Image Acquisition &amp; Data Pre-Processing Module  200 . Then at block  320 , module  300  characterizes the gestures from the feature data. And at block  330 , module  300  associates the gestures with particular pre-determined gestures, according to an embodiment of the disclosure. As the output of process  300 A, at block  330  a gesture library is constructed, in which each gesture has a particular characterization in terms of the data generated by Image Acquisition &amp; Data Pre-Processing Module  200 . 
     Blocks  320  and  330  of  FIG. 3A  may contain the blocks  FIG. 3B , described in more detail below and found at  FIG. 1  from U.S. patent application Ser. No. 11/866,280. Alternatively, block  310  of  FIG. 3A  may contain the blocks of  FIG. 3C  described in more detail below and also found at  FIG. 2  from U.S. patent application Ser. No. 11/866,280. In one embodiment, blocks  320  and  330  are performed as an offline task. 
     In order to classify a user&#39;s movements as a particular gesture, the user&#39;s movements are compared to a known set of gestures catalogued and stored in a gesture library. For each gesture in the library, baseline or “ground truth” data is first generated in a pre-processing step for each gesture. The “ground truth” data is then used as a baseline against which a user&#39;s movements are compared in order to classify the movements as a particular gesture. Data characterizing the relative positions of the feature points of interest over several images in a sequence are used for the comparison.  FIG. 3B  shows one method  300 B by which “ground truth” data may be obtained for the gesture library. 
     In step  110 A, at least one subject is recorded performing a gesture of interest multiple times. A sensor is placed on each feature point of interest on the subject&#39;s body, and motion capture equipment is used to record the subject&#39;s movements in a sequence of images. Feature points of interest may include joints and locations corresponding to, for example, the subject&#39;s left hand, left elbow, left shoulder, or head. It will be apparent to a person skilled in the art that many other locations on a subject&#39;s body may also be feature points of interest. The output of step  110 A is a set of three-dimensional points with each point corresponding to one feature point in each image in the sequence. 
     In step  120 A, the data from the motion capture sessions are post-processed by manually cleaning and smoothing the data using standard techniques for processing motion capture data. It will be apparent to a person skilled in the art that other post-processing steps may also be performed. The data is then averaged in step  125 A over the multiple times that the gesture is performed in order to minimize bias. In a preferred embodiment, many different subjects are recorded performing the gesture, and the gestures of the different subjects are averaged to prevent overfitting the ground truth data to one person. 
     A similarity measure is a function that quantitatively compares the similarity of two gesture sets with each other. The higher the similarity measure value, the more similar a person&#39;s movements are to a known gesture that the movements are being compared to. In step  130 A, a threshold value is calculated for the gesture such that if a similarity measure comparing the gesture to a person&#39;s movements is greater than a threshold value for that particular gesture, it is likely that the person&#39;s movements have been identified as that gesture. 
     Step  140 A queries whether another gesture is to be added to the gesture library. If so, the above steps are repeated beginning at step  110 A with the recording of at least one subject performing the new gesture. If no further gestures are to be added to the library, then the gesture library is complete. 
       FIG. 3C  shows an alternative method  300 C by which “ground truth” data for a gesture and its corresponding gesture threshold value may be obtained for a gesture library. In step  210 A, a videocamera capable of recording color and depth images is used to record at least one subject performing a gesture of interest several times. In step  220 A, the positions of the feature points of interest are manually marked on the sequences of color and depth images. In other embodiments, marking the points of interest may be automated or semi-automated. For example, automatic tracking can be run on the depth images from the videocamera to determine points of interest, and in some embodiments the automatically identified points of interest can be corrected manually. In stage  230 A, three-dimensional coordinates of each feature point of interest are calculated for each color-depth pair of images in the sequence of images capturing the gesture. Post-processing of the data occurs in step  240 A. Post-processing steps that may be performed include smoothing the data temporally and spatially. It will be apparent to a person skilled in the art that other post-processing steps may also be performed. 
     The data is then averaged in step  250 A over the multiple times that the gesture is performed in order to minimize bias. In a preferred embodiment, many different subjects are recorded performing the gesture, and the gestures of the different subjects are averaged to prevent overfitting the ground truth data to one person. 
     In step  260 A, a threshold value is calculated for the gesture such that if a similarity measure comparing the gesture to a person&#39;s movements is greater than a threshold value for that particular gesture, it is likely that the person&#39;s movements have been identified as that gesture. 
     Step  270 A queries whether another gesture is to be added to the gesture library. If so, the above steps are repeated beginning at step  210 A with the recording of at least one subject performing a new gesture. If no further gestures are to be added to the library, then the gesture library is complete. 
     Any technique used for automatically classifying data can be used, including supervised as well as unsupervised machine learning techniques. Data classification techniques include, but are not limited to, SVM (support vector machines), Hidden Markov Models (HMMs), and k-means clustering. For example, SVM could be used to find the “optimal separation” between two classes of data points (“the desired gesture” and “the not desired gesture”), and the derived decision function could be applied to the candidate gesture to determine which class the candidate gesture falls into. 
       FIG. 4A  depicts a flow diagram illustrating an exemplary process  400 A for using a binary gesture recognition technique to determine whether the gesture being searched for was performed or not performed, according to an embodiment of the disclosure. In one embodiment, the binary gesture recognition technique can introduce delay in a game by waiting until the full time-dependent sequence is received from the Image Acquisition and Data Pre-Processing Module  200  before computing whether a gesture from the gesture library was performed. 
     At block  410 , the Binary Gesture Recognition Module  400  receives feature data from the Image Acquisition &amp; Pre-processing Module  200  that describe the features. Then at block  420 , the player&#39;s gestures corresponding to gestures in the gesture library are detected by module  400 . The output from block  420  is a detected gesture. In one embodiment, block  420  can contain the blocks of  FIG. 3  from U.S. patent application Ser. No. 11/866,280, as shown in  FIG. 4B  and described below. 
     The color and depth images acquired in steps  310 A and  320 A are used to locate feature points of interest on the user&#39;s body in step  330 A. Feature points of interest may include joints and locations corresponding to, for example, the user&#39;s left hand, left elbow, left shoulder, or head. It will be apparent to a person skilled in the art that many other locations on a user&#39;s body may also be feature points of interest. The present invention is intended to be able to identify gestures made by any part or parts of a user&#39;s body. 
     In the step  340 A, three-dimensional coordinates for each one of the feature points of interest are computed from the color and depth images. The coordinate locations for each of the feature points of interest are stored in step  350 A for the frame corresponding to the co-acquired color and depth images. 
     Classification of a user&#39;s recorded movements is accomplished by comparing the movements with each of the gestures stored in a gesture library. Each gesture in the library consists of a sequence of images covering the period of time required to perform the gesture, with a uniform time lapse occurring between images in the sequence. Each gesture is associated with a minimum number of sequential images sufficient to capture the entire movement of the gesture. Thus, a quick gesture like a finger snap requires fewer sequential images, while a gesture that takes a longer time to perform, for example, a handshake, requires more sequential images. Let the gesture in the library which takes the shortest period of time to perform be captured by a number of sequential images called MIN GESTURE IMAGES. Let the gesture in the library which takes the longest period of time to perform be captured by a number of sequential images called MAX GESTURE IMAGES. Thus, capturing MAX GESTURE IMAGES sequential images will be sufficient to capture any gesture in the library. 
     At decision point  355 A, if MIN GESTURE IMAGES sequential images have not been acquired and stored, the process returns to steps  310 A and  320 A where another set of color and depth images is co-acquired and appended to the sequence of images being analyzed. If at least MIN GESTURE IMAGES sequential images have been stored for analysis, step  360 A makes a quantitative comparison of the user&#39;s movements with each gesture in the library requiring no more than the number of currently stored images. For example, if gesture A requires eight images to capture, gesture B requires nine images to capture, and gesture C requires ten images to capture, and there are currently nine stored images, a comparison of the eight most recently acquired images will be made with gesture A, while a comparison of all nine images will be made with gesture B. Gesture C will not be used for a comparison at this point in the algorithm because not enough images have been acquired yet. 
     The quantitative comparison is made through the use of a similarity measure. A similarity measure calculates how similar two gesture data sets are to each other; the higher the similarity measure value is, the more similar the two gesture data sets are. A sample similarity measures is described in more detail below. Thus, in step  360 A, a set of similarity measure values are obtained by comparing the user&#39;s movements to each gesture in the library requiring no more than the number of currently stored images. 
     Then in step  370 A, each of the similarity measure values in the set are compared to the threshold value for the particular gesture which was used to obtain the similarity measure value. Gestures which result in a similarity measure value greater than the gesture&#39;s pre-calculated threshold value, if any, are identified and passed to decision point  375 . 
     At decision point  375 A, if at least one gesture has been identified which produced a similarity measure value greater than the corresponding threshold value, the gesture in the library which produced the highest similarity measure value is identified as the gesture that the user made and is output at step  390 A. Then in step  395 A, the sequence of images acquired in steps  310 A and  320 A is deleted, and the process subsequently returns to steps  310 A and  320 A to obtain a new set of color and depth images to identify the next movements made by the user. 
     At decision point  375 A, if no gestures were identified which produced a similarity measure value greater than the corresponding threshold value, then no known gesture was detected in the time period spanned by the sequential images co-acquired in steps  310 A and  320 A and used to calculate the similarity measure values in step  360 A. The process flows to decision point  378 A where it is determined whether MAX GESTURE IMAGES sequential images have been acquired. If the number of images that have been stored is less than MAX GESTURE IMAGES sequential images, the process returns to steps  310 A and  320 A where another set of color and depth images of the user is co-acquired and appended to the sequence of images for analysis. 
     If at decision point  378 A the MAX GESTURE IMAGES sequential images have already been acquired and analyzed, the earliest co-acquired color and depth images in the sequence of images stored for analysis are deleted. Then the process returns to steps  310 A and  320 A where another set of color and depth images of the user is co-acquired and appended to the sequence of images for analysis. 
     At block  430 , the Game Engine Module of the system generates feedback for the player based on the gestures detected at block  420 . The Game Engine Module essentially controls the game application with which the player interacts. At block  440 , the system displays the generated feedback on a display for the player, for example, adjusting the player&#39;s score according to the player&#39;s performance. 
       FIG. 5  depicts a flow diagram illustrating an exemplary process  500 A of verifying whether the player is performing a particular gesture or not over a period of time (i.e., over a sequence of frames), and determining how accurately the player is performing the prompted gesture, according to an embodiment of the disclosure. 
     At block  510 , the Real-Time Gesture Recognition Module  500  receives feature data from the Image Acquisition &amp; Pre-processing Module  200 . The Real-Time Gesture Recognition Module updates the player&#39;s progress in performing the gesture in real-time, for example, after every frame. At block  520 , the Game Engine Module of the system selects a gesture of interest and prompts the user to perform the gesture. 
     At block  530 , a cumulative tracking score (CTS) is set to 0. In one embodiment, the CTS is updated at every frame. However, the CTS can be updated at other intervals, for example every second frame. Next, the feature data received at block  510  is compared to the gesture of interest selected at block  520 , and a numerical value corresponding to how closely the player&#39;s movements match the gesture of interest is computed. One way of comparing the data from block  510  with the gesture of interest data from block  520  is to use a similarity measure. 
     One exemplary similarity measure is as follows: Consider, for example, x(i,j) is the pre-determined location of joint i at time j, according to the gesture of interest, and y(i,j) is the value obtained from block  510  for joint i at time j, that is, the location of joint i at time j for the gesture of interest. Let w(i) be the weights per joint, and u(j) the weights per time. Then, an example similarity measure is: 
                 S     u   ,   w       ⁡     (       x   -&gt;     ,     y   -&gt;       )       =       ∑     j   =   1     n     ⁢       u   ⁡     (   j   )       ⁢       ∑     i   =   1     m     ⁢       w   ⁡     (   i   )       ⁢              x   ⁡     (     i   ,   j     )       -     y   ⁡     (     i   ,   j     )              .                   
In one embodiment, the weights u(j) and w(i) can be assigned on an ad hoc basis. At block  540 , the similarity measure (in the above example, S u,w (x, y)) is calculated per frame, and at block  545 , the cumulative tracking score is incremented by the value of S u,w (x, y).
 
     At decision block  550 , the system determines if the cumulative tracking score remains within a given threshold. If the CTS remains within a given threshold (block  550 —Yes), this indicates that the movements of the player are sufficiently close to those characterized by the gesture of interest, and the process continues to block  555  where information that the player is performing the gesture of interest is sent to the Game Engine Module. At block  570 , the Game Engine Module provides feedback to the player through a display based upon the supplied information. 
     Then at decision block  575 , the system determines if there is another frame to analyze from the feature data received from module  200  at block  510 . If there is another frame (block  575 —Yes), the process returns to block  540  to calculate a similarity measure for the next frame. If there are no other frames to analyze (block  575 —No), the process returns to block  510  to receive more feature data from module  200 . 
     If the CTS does not remain within a given threshold (block  550 —No), at block  560 , the CTS is set to 0. Then at block  565  the information that the player is not performing the gesture of interest is sent to the Game Engine Module, and the process continues to block  570  as described above. 
       FIG. 6  is a block diagram  600  of one embodiment of the gesture classification system incorporated into an interactive program. The video camera equipment  610  captures a user&#39;s movements. The video camera equipment  610  takes simultaneous color and depth images of the user, and the images are sent to the processor  620  for processing. 
     The processor  620  locates feature points of interest in the color and depth images, calculates three-dimensional coordinates for each feature point in the co-acquired color and depth images, stores the coordinates in memory  630  for processing, ensures the minimum number of images have been acquired, calculates similarity measures by comparing the movements with each gesture in the database  640 , identifies gestures that have similarity measures greater than the threshold value for the database gesture that the movements have been compared with, identifies the highest similarity measure obtained, prompts the video camera equipment  610  to acquire additional images, controls the memory  630  to delete processed images, and outputs identified gestures to the display  650 , thus providing feedback to the user. The processor  620  also runs the interactive program which the user experiences virtually through the display  650 . 
     The display  650  presents an image of the user performing the gesture identified by the processor  620 . The image of the user is incorporated into the virtual environment of the interactive program which is also presented by display  650 . 
       FIG. 7  is a block diagram  700  of one embodiment of the gesture classification system incorporated into an interactive program accessed by multiple players over a network. 
     Multiple players may access the same interactive program from different locations.  FIG. 7  shows three separate sites  740 ,  750 , and  760  from which users access the same virtual environment, but any number of users from any number of sites may participate in the interactive program. Each site  740 ,  750 , and  760  has video camera equipment  742 ,  752 , and  762  which take simultaneous color and depth images of a user at that location, and the images are sent to the processor  720  for processing. If more than one user is at the same site, video camera equipment dedicated to each user at the site should be available. All the users at the same site may share a display or have their own individual displays  744 ,  754 , and  764 . However, all the displays are capable of showing images of all the users at the different sites participating in the same virtual environment. 
     The images obtained by the video camera equipment  742 ,  752 , and  762  from the different sites  740 ,  750 , and  760  are sent over a network  770  to the processor  720 . The processor  720 , memory  730 , and gesture database  710  function in the same manner as described in  FIG. 6  above. However, with multiple users participating in the same interactive program, the processor  720  must process the images captured for each user. Alternatively, the processor  720  may have sub-processors dedicated to individual users, and each sub-processor may access an independent memory within the memory  730 . It will be apparent to a person skilled in the art that different hardware structures may implement the functions of the processor  720  and the memory  730  to optimize the response time. 
     The processor  720  also runs the interactive program which the users experience virtually through the displays  744 ,  754 , and  764 . The images of all the users are incorporated into the virtual environment of the interactive program which is presented by each display  744 ,  754 , and  764 . Signals are sent by the processor  720  to the displays  744 ,  754 , and  764  along the network  770 .