Hand pointing estimation for human computer interaction

• US 8,971,572 B1
• Filed: 08/10/2012
• Issued: 03/03/2015
• Est. Priority Date: 08/12/2011
• Status: Active Grant

• Current Assignee: The Research Foundation for The State University of New York (State University of New York)
• Original Assignee: The Research Foundation for The State University of New York (State University of New York)
• Inventors: Yin, Lijun, Canavan, Shaun, Hu, Kaoning
• Primary Examiner(s): AZARIAN, SEYED H
• Application Number:
  US13/571,645
• Time in Patent Office: 935 Days
• Field of Search: 382/100, 382/103, 382106-107, 382/162, 382/168, 382/173, 382/181, 382/199, 382/209, 382/219, 382/232, 382/254, 382/274, 382/276, 382285-297, 382/305, 382/312, 345/173, 345/677, 345/163, 715/773, 715/784
• US Class Current: 382/103
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0304 Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06T 7/174 involving the use of two or more images
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/446 using Haar-like filters, e.g. using integral image techniques
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/7557 based on appearance, e.g. active appearance models [AAM]
• CPC Class Codes: G06V 40/107 Static hand or arm
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G06V 40/28 Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113

Abstract

Hand pointing has been an intuitive gesture for human interaction with computers. A hand pointing estimation system is provided, based on two regular cameras, which includes hand region detection, hand finger estimation, two views'"'"' feature detection, and 3D pointing direction estimation. The technique may employ a polar coordinate system to represent the hand region, and tests show a good result in terms of the robustness to hand orientation variation. To estimate the pointing direction, Active Appearance Models are employed to detect and track, e.g., 14 feature points along the hand contour from a top view and a side view. Combining two views of the hand features, the 3D pointing direction is estimated.

Claims

1. A method for estimating a finger pointing direction using an active appearance model, which tracks a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied to formulate a statistical model of the hand, comprising:

detecting a hand in each of at least two images acquired from different angles with at least one automated processor, comprising generating Haar-like features from the at least two images, implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand, and locating a center of the hand and a position of the wrist in each image;

warping the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand;

applying the active appearance model to find a best fit for visible landmarks of the hand in each image;

combining the best fit for the visible landmarks of the hand in each image to the active appearance model to infer a three dimensional position of each visible landmark in each image;

determining at least two visible features of a finger extending from the hand, to define a pointing gesture in each image; and

determining with the at least one automated processor, at least one of a three dimensional pointing vector of the finger and a target of the pointing gesture.

2. The method according to claim 1, wherein the statistical model of the hand comprises a shape of the hand and a texture of the hand.

3. The method according to claim 1, wherein the at least two images are color images, and wherein color sensitive image processing is employed to distinguish the hand from a background.

4. The method according to claim 1, wherein the at least two images are color images, and wherein color sensitive image processing is employed to define a wrist location as a location on the hand having a most similar surrounding color consistent with skin tones.

5. The method according to claim 1, wherein the at least two visible features of the finger are selected based on a reliability of a resulting three dimensional pointing vector.

6. The method according to claim 1, further comprising providing a user feedback display of a target of the pointing gesture.

7. The method according to claim 1, wherein the determining at least one of the three dimensional pointing vector of the finger and the target of the pointing gesture represent a statistical averaging over time derived from the pointing gesture in each image.

8. The method according to claim 1, wherein the best fit for visible landmarks of the hand in each image to the active appearance model is updated for respective sequences of the at least two images, to track a movement of the hand.

9. The method according to claim 1, wherein the at least two images comprise at least two video signals, each representing a time sequence of images.

10. The apparatus according to claim 1, wherein the at least two images are color images, and wherein the at least one processor is configured to perform color sensitive image processing to distinguish the hand from a background and to define a wrist location as a location on the hand having a most similar surrounding color consistent with skin tones.

11. A method for estimating a finger pointing direction using an active appearance model, which tracks a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied to formulate a statistical model of the hand, comprising:

capturing at least two images of a hand from different directions;

detecting the hand in each image and locating a center of the hand with at least one automated processor, comprising generating Haar-like features from the at least two images, and implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand;

determining a position of the wrist in each image;

warping the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand;

applying the active appearance model to find a best fit for the hand in each image;

combining the fit of the hand in each image to the active appearance model to infer a three dimensional position of each visible point in an image;

determining two points at different distances along a finger extending from the hand, to define a pointing vector in the at least two images, wherein the two points are selected based on a reliability of the resulting vector; and

determining with at least one automated processor, a three dimensional pointing vector of the finger.

12. The method according to claim 11, wherein the statistical model of the hand comprises a shape of the hand and a texture of the hand.

13. The method according to claim 11, wherein the at least two images are color images, and wherein color sensitive image processing is employed to distinguish the hand from a background, and color sensitive image processing is employed to define a wrist location as a location on the hand having a most similar surrounding color consistent with skin tones.

14. The method according to claim 11, wherein the at least two visible features of the finger are selected based on a reliability of a resulting three dimensional pointing vector.

15. The method according to claim 11, further comprising providing a user feedback display of a target along the three dimensional pointing vector of the finger.

16. The method according to claim 11, wherein the determined three dimensional pointing vector of the finger represents a statistical averaging over time.

17. The method according to claim 11, wherein the best fit for the hand in each image to the active appearance model is updated for respective sequences of the at least two images, to track a movement of the hand.

18. The method according to claim 11, wherein the at least two images comprise at least two video signals, each representing a time sequence of images.

19. An apparatus for estimating a finger pointing direction, comprising:

a memory configured to store parameters of an active appearance model of a hand, which includes a plurality of landmarks on the hand, corresponding to landmarks of hands in a training image set to which a principal component analysis is applied;

an input configured to receive at least two images acquired from different angles;

at least one processor configured to receive the input, access the memory, and produce an output based on the at least two images and the parameters in the memory; and

an output port, communicating data responsive to a finger pointing direction of the hand,

wherein the at least one processor is further configured to;

detect a hand in each of the at least two images acquired from different angles, by generating Haar-like features from the at least two images, and implementing a cascade detector using AdaBoost, wherein portions of each image are scanned over different translations, scales and rotations to find a best fit for the hand, and locating a center of the hand and a position of the wrist in each image;

warp the detected hand in each image from a Cartesian Coordinate representation to a Polar Coordinate representation, with the center of the hand at the pole, and the polar angle determined by a position of the wrist with respect to the center of the hand;

apply the active appearance model, based on the parameters in the memory, to find a best fit for visible landmarks of the hand in each image;

combine the best fit for the visible landmarks of the hand in each image to the active appearance model to infer a three dimensional position of each visible landmark in each image;

determine at least two visible features of a finger extending from the hand, to define a pointing gesture in each image; and

determine a pointing direction comprising at least one of a three dimensional pointing vector of the finger and a target of the pointing gesture.

20. The apparatus according to claim 19, wherein the principal component analysis is applied to formulate a statistical model of the hand which comprises a shape of the hand and a texture of the hand.