HIERARCHICAL FACE RECOGNITION TRAINING METHOD AND HIERARCHICAL FACE RECOGNITION METHOD THEREOF

• US 20100074479A1
• Filed: 01/08/2009
• Published: 03/25/2010
• Est. Priority Date: 09/19/2008
• Status: Active Grant

• Current Assignee: Altek Semiconductor Corp.
• Original Assignee: Altek Corporation
• Inventors: CHOU, Hong-Long, YANG, Tai-Chang, CHANG, Yin-Pin
• Granted Patent: US 8,306,282 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06F 18/24323 Tree-organised classifiers
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 30/242 Division of the character sequences into groups prior to recognition; Selection of dictionaries
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions

Abstract

A hierarchical face recognition training method and a hierarchical face recognition method thereof for performing a face feature recognition on an image under detection. The method includes a training process and a recognition process. The recognition method includes the steps. A plurality of training samples is obtained. The training samples are subdivided into a plurality of sub-image categories according to a plurality of angle intervals, and the training of a plurality of face features performs on a corresponding sub-image detector of each of the sub-image categories. The training measures performed repeatedly to generate sub-image categories at a sub-level of the sub-image categories. The training method includes the steps. An image under detection is loaded. A similarity of each of sub-image detectors compares according to the image under detection, and the sub-image detector having the highest similarity is selected. The face recognition measures performed repeatedly on the selected sub-image detector.

Claims

1. A hierarchical training method for a face recognition, using a computer device to perform a face feature training of sub-image detectors at each level on a plurality of training samples, the method comprising:

obtaining the training samples;

performing a training measure, subdividing the training samples into a plurality of sub-image categories according to a plurality of angle intervals, and performing the training of a plurality of face features on a corresponding sub-image detector of each of the sub-image categories;

repeatedly performing the training measure on each of the sub-image categories, so as to generate sub-image categories at a sub-level of the sub-image categories until a subdivision condition is satisfied.

2. The hierarchical training method for a face recognition according to claim 1, after obtaining the training samples, further comprising:

performing a face recognition to recognize whether the training samples comprise a face portion;

performing the training measure if the training sample comprises a face portion; and

subdividing the training sample into an exceptional category if the training sample does not comprise a face portion.

3. The hierarchical training method for a face recognition according to claim 1, wherein an entire face of the training sample is used as the face feature.

4. The hierarchical training method for a face recognition according to claim 1, wherein a relative position of at least one of an eyebrow, an ear, an eye, a nose, and a mouth is selected from the face of the training sample as the face feature.

5. The hierarchical training method for a face recognition according to claim 1, wherein the subdivision condition is determined by a number of the generated levels of the sub-image categories.

6. The hierarchical training method for a face recognition according to claim 1, wherein the subdivision condition is determined by a number of the sub-image categories at each level.

7. A hierarchical face recognition method performed on an image under detection by the use of sub-image detectors according to claim 1, using a computer device to perform a face feature recognition of each of the sub-image detectors on the image under detection, the method comprising:

loading an image under detection;

respectively performing the face recognition measure on the image under detection by each of the sub-image detectors at the same level, so as to obtain a similarity of each of the sub-image detectors to the image under detection, and selecting the sub-image detector having the highest similarity from the sub-image detectors; and

repeatedly performing the face recognition measure on the image under detection by the selected sub-image detector until the image under detection completes the sub-image detectors at the last level.

8. The hierarchical face recognition method performed on an image under detection by use of sub-image detectors according to claim 7, after obtaining the similarity, further comprising:

performing a face recognition measure on the image under detection by the sub-image detector having the highest similarity; and

subdividing the image under detection into an exceptional category if a processing result of the facial recognition measure satisfies a critical condition.

9. The hierarchical face recognition method performed on an image under detection by use of sub-image detectors according to claim 7, wherein when performing the face recognition measure, the sub-image detector selects at least one face feature as a basis for processing the similarity.

10. A hierarchical face recognition method for an image under detection, using a computer device to perform a face feature recognition of each of sub-image detectors on the image under detection, the method comprising:

obtaining a plurality of training samples;

performing a training measure, subdividing the training samples into a plurality of sub-image categories according to a plurality of angle intervals, and performing the training of a plurality of face features on a corresponding sub-image detector of each of the sub-image categories;

repeatedly performing the training measure on each of the sub-image categories, so as to generate sub-image categories at a sub-level of the sub-image categories until a subdivision condition is satisfied;

loading an image under detection;

performing a face recognition measure, comparing a similarity of each of the sub-image detectors at the same level to the image under detection according to the image under detection, and selecting the sub-image detector having the highest similarity from the same level; and

repeatedly performing the face recognition measure on the image under detection by the selected sub-image detector until the image under detection completes the sub-image detectors at the last level.

11. The hierarchical face recognition method for an image under detection according to claim 10, after obtaining the training samples, further comprising:

performing a face recognition to recognize whether the training samples comprise a face portion;

performing the training measure if the training sample comprises a face portion; and

subdividing the training sample into an exceptional category if the training sample does not comprise a face portion.

12. The hierarchical face recognition method for an image under detection according to claim 10, wherein an entire face of the training sample is used as the face feature.

13. The hierarchical face recognition method for an image under detection according to claim 10, wherein a relative position of at least one of an eyebrow, an ear, an eye, a nose, and a mouth is selected from the face of the training sample as the face feature.

14. The hierarchical face recognition method for an image under detection according to claim 10, wherein the subdivision condition is determined by a number of the generated levels of the sub-image categories.

15. The hierarchical face recognition method for an image under detection according to claim 10, wherein the subdivision condition is determined by a number of the sub-image categories at each level.