3D Hand Gesture Image Recognition Method and System Thereof

• US 20180088671A1
• Filed: 09/27/2016
• Published: 03/29/2018
• Est. Priority Date: 09/27/2016
• Status: Abandoned Application

• Current Assignee: National Kaohsiung University of Applied Sciences
• Original Assignee: National Kaohsiung University of Applied Sciences
• Inventors: Wang, Jing-Wein
• Application Number:
  US15/277,013
• Publication Number:
  US 20180088671A1
• Time in Patent Office: Days
• 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: G06V 40/107 Static hand or arm

Abstract

A 3D hand gesture recognition system includes a light field capturing unit, a calculation unit and an output unit. The light field capturing unit is provided to capture a hand gesture action to obtain a 3D hand gesture image. The calculation unit connects with the light field capturing unit and is provided to project the 3D hand gesture image to a predetermined space to obtain eigenvectors which are compared with the samples to classify and recognize a signal of the 3D hand gesture image. The output unit connects with the calculation unit to output the signal of the 3D hand gesture image to a predetermined device.

Claims

1. A 3D hand gesture image recognition method comprising:

operating a light field capturing unit to capture a hand gesture action to thereby obtain at least one 3D hand gesture image;

projecting the at least one 3D hand gesture image to a predetermined space to obtain at least one set of eigenvectors; and

comparing the eigenvectors with a plurality of samples to classify and recognize a signal of the 3D hand gesture image.

2. The method as defined in claim 1, wherein the 3D hand gesture image includes 2D plane information and depth information.

3. The method as defined in claim 1, wherein the 3D hand gesture image is a 3D contour image, a 3D solid RGB image or combination thereof.

4. The method as defined in claim 3, wherein the 3D solid RGB image is further projected to the predetermined space by a projection color space method, thereby obtaining R channel image information, G channel image information and B channel image information.

5. The method as defined in claim 1, wherein the 3D hand gesture image is projected to the predetermined space by principal component analysis.

6. The method as defined in claim 1, wherein the eigenvectors are compared with the plurality of samples by a k-nearest neighbor method to classify and recognize the signal of the 3D hand gesture image.

7. A 3D hand gesture image recognition method comprising:

operating a light field capturing unit to capture a series of hand gesture actions to thereby obtain a first 3D hand gesture image and a second 3D hand gesture image;

projecting the first 3D hand gesture image and the second 3D hand gesture image to a predetermined space to obtain a first set of first eigenvectors and a second set of second eigenvectors;

comparing the first eigenvectors and the second eigenvectors with a plurality of samples to classify and recognize a first signal of the first 3D hand gesture image and a second signal of the second 3D hand gesture image; and

identifying the second signal of the second 3D hand gesture image with the first signal of the first 3D hand gesture image.

8. The method as defined in claim 7, wherein the first 3D hand gesture image and the second 3D hand gesture image include 2D plane information and depth information.

9. The method as defined in claim 7, wherein the first 3D hand gesture image and the second 3D hand gesture image are 3D contour images, 3D solid RGB images or combination thereof.

10. The method as defined in claim 9, wherein the 3D solid RGB image is further projected to the predetermined space by a projection color space method, thereby obtaining R channel image information, G channel image information and B channel image information.

11. The method as defined in claim 7, wherein the first 3D hand gesture image and the second 3D hand gesture image are projected to the predetermined space by principal component analysis.

12. The method as defined in claim 7, wherein the first eigenvectors and the second eigenvectors are compared with the plurality of samples by a k-nearest neighbor method to classify and recognize the signal of the 3D hand gesture image.

13. A 3D hand gesture image recognition system comprising:

a first light field capturing unit provided to capture a hand gesture action to thereby obtain a first 3D hand gesture image;

a calculation unit connected with the first light field capturing unit and provided to project the first 3D hand gesture image to a predetermined space to obtain a first set of first eigenvectors, with further comparing the first eigenvectors with a plurality of samples to classify and recognize a first signal of the first 3D hand gesture image; and

an output unit connected with the calculation unit and provided to output the first signal of the first 3D hand gesture image to a predetermined device.

14. The system as defined in claim 13, wherein the first 3D hand gesture image includes 2D plane information and depth information.

15. The system as defined in claim 13, wherein the first 3D hand gesture image is a 3D contour image, a 3D solid RGB image or combination thereof.

16. The system as defined in claim 15, wherein the 3D solid RGB image is further projected to the predetermined space by a projection color space method to obtain R channel image information, G channel image information and B channel image information.

17. The system as defined in claim 13, wherein the first 3D hand gesture image is projected to the predetermined space by principal component analysis.

18. The system as defined in claim 13, wherein the first eigenvectors are compared with the plurality of samples by a k-nearest neighbor method to classify and recognize the first signal of the first 3D hand gesture image.

19. The system as defined in claim 13, wherein a second light field capturing unit is provided to capture the hand gesture action to thereby obtain a second 3D hand gesture image which is further projected, classified and recognized to obtain a second signal of the second 3D hand gesture image.

20. The system as defined in claim 19, wherein the second signal of the second 3D hand gesture image is identified with the first signal of the first 3D hand gesture image.