Pairwise feature learning with boosting for use in face detection

• US 7,844,085 B2
• Filed: 06/07/2007
• Issued: 11/30/2010
• Est. Priority Date: 06/07/2007
• Status: Active Grant

• Current Assignee: 138 East LCD Advancements Limited (Vector Capital Corporation)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Lu, Juwei, Zhou, Hui
• Primary Examiner(s): PERUNGAVOOR, SATHYANARAYA V
• Application Number:
  US11/759,460
• Publication Number:
  US 20080304714A1
• Time in Patent Office: 1,272 Days
• Field of Search: 382/118
• US Class Current: 382/118
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation

Abstract

Systems and methods for training an AdaBoost based classifier for detecting symmetric objects, such as human faces, in a digital image. In one example embodiment, such a method includes first selecting a sub-window of a digital image. Next, the AdaBoost based classifier extracts multiple sets of two symmetric scalar features from the sub-window, one being in the right half side and one being in the left half side of the sub-window. Then, the AdaBoost based classifier minimizes the joint error of the two symmetric features for each set of two symmetric scalar features. Next, the AdaBoost based classifier selects one of the features from the set of two symmetric scalar features for each set of two symmetric scalar features. Finally, the AdaBoost based classifier linearly combines multiple weak classifiers, each of which corresponds to one of the selected features, into a stronger classifier.

Claims

1. A method for detecting an object using an AdaBoost based classifier, such as a face, in a digital image, the method comprising the following acts:

selecting a sub-window of a digital image from a set of training sub-windows that are labeled as face or non-face sub-windows;

extracting multiple sets of two symmetric scalar features from the sub-window;

minimizing the joint error of the two symmetric features for each set of two symmetric scalar features;

selecting one of the features from the set of two symmetric scalar features for each set of two symmetric scalar features; and

linearly combining multiple weak classifiers, each of which corresponds to one of the selected features, into a stronger classifier.

2. The method as recited in claim 1, wherein the act of minimizing the joint error of the two symmetric features comprises minimizing the joint error, E(f_i, f_i), of the two symmetric features, (f_i, f_i), in accordance with Equation 12, in order to arrive at the optimal pair of features, (f*, f*), in accordance with Equation 13.

3. The method as recited in claim 1, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises randomly selecting one of the features from the set of two symmetric scalar features.

4. The method as recited in claim 1, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises selecting one of the features from the set of two symmetric scalar features according to the locations of features in the left or right half side of the sub-window.

5. The method as recited in claim 4, wherein the act of selecting one of the features from the set of two symmetric scalar features according to the locations of features in the left or right half side of the sub-window comprises selecting alternating left and right half side features.

6. The method as recited in claim 1, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises selecting one of the features from the set of two symmetric scalar features according to which feature has the smaller error.

7. The method as recited in claim 1, wherein the AdaBoost based classifier is a gentle AdaBoost based classifier.

8. The method as recited in claim 1, wherein the AdaBoost based classifier is a discrete AdaBoost based classifier.

9. The method as recited in claim 1, wherein the AdaBoost based classifier is a real AdaBoost based classifier.

10. One or more non-transitory computer-readable media having computer-readable instructions thereon which, when executed, implement a method for training an AdaBoost based classifier for detecting faces in a digital image, the method comprising the following acts:

selecting a sub-window of a digital image from a set of training sub-windows that are labeled as face or non-face sub-windows;

extracting multiple sets of two symmetric scalar features from the sub-window, one being in the right half side and one being in the left half side of the sub-window;

minimizing the joint error of the two symmetric features for each set of two symmetric scalar features;

selecting one of the features from the set of two symmetric scalar features for each set of two symmetric scalar features; and

linearly combining multiple weak classifiers, each of which corresponds to one of the selected features, into a stronger classifier.

11. The one or more computer-readable media recited in claim 10, wherein the act of minimizing the joint error of the two symmetric features comprises minimizing the joint error, E(f_i, f_i), of the two symmetric features, (f_i, f_i), in accordance with Equation 12, in order to arrive at the optimal pair of features, (f*, f*), in accordance with Equation 13.

12. The one or more computer-readable media recited in claim 10, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises randomly selecting one of the features from the set of two symmetric scalar features.

13. The one or more computer-readable media recited in claim 10, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises selecting one of the features from the set of two symmetric scalar features according to the locations of features in the left or right half side of the sub-window.

14. The one or more computer-readable media recited in claim 13, wherein the act of selecting one of the features from the set of two symmetric scalar features according to the locations of features in the left or right half side of the sub-window comprises selecting alternating left and right half side features.

15. The one or more computer-readable media recited in claim 10, wherein the act of selecting one of the features from the set of two symmetric scalar features comprises selecting one of the features from the set of two symmetric scalar features according to which feature has the smaller error.

16. The one or more computer-readable media recited in claim 10, wherein the AdaBoost based classifier is a gentle AdaBoost based classifier.

17. The one or more computer-readable media recited in claim 10, wherein the AdaBoost based classifier is a discrete AdaBoost based classifier.

18. The one or more computer-readable media recited in claim 10, wherein the AdaBoost based classifier is a real AdaBoost based classifier.