Hierarchical face recognition training method and hierarchical face recognition method thereof
First Claim
1. A hierarchical training method for 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 by subdividing the training samples into a plurality of sub-image categories according to a plurality of angle intervals, and performing the face feature training on a corresponding sub-image detector of each of the sub-image categories; and
performing the training measure for each of the sub-image categories, so as to generate sub-image categories at a lower level than a current level for said each sub-image categories, through which one of the plurality of angle intervals corresponding to said each sub-image category is subdivided into a plurality of angle intervals at the lower level, each angle interval at the lower level corresponding to one of the plurality of sub-image categories at the lower level, and repeating the training measure for each sub-image category at the rawer level until a subdivision condition is satisfied, whereinthe sub-image categories of all levels form a tree structure.
2 Assignments
0 Petitions
Accused Products
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.
-
Citations
15 Claims
-
1. A hierarchical training method for 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 by subdividing the training samples into a plurality of sub-image categories according to a plurality of angle intervals, and performing the face feature training on a corresponding sub-image detector of each of the sub-image categories; and performing the training measure for each of the sub-image categories, so as to generate sub-image categories at a lower level than a current level for said each sub-image categories, through which one of the plurality of angle intervals corresponding to said each sub-image category is subdivided into a plurality of angle intervals at the lower level, each angle interval at the lower level corresponding to one of the plurality of sub-image categories at the lower level, and repeating the training measure for each sub-image category at the rawer level until a subdivision condition is satisfied, wherein the sub-image categories of all levels form a tree structure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
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 by subdividing the training samples into a plurality of sub-image categories according to a plurality of angle intervals, and performing a face feature training on a corresponding sub-image detector of each of the sub-image categories; performing the training measure for each of the sub-image categories, so as to generate sub-image categories at a lower level than a current level for said each sub-image categories, through which one of the plurality of angle intervals corresponding to said each sub-image category is subdivided into a plurality of angle intervals at the lower level, each angle interval at the lower level corresponding to one of the plurality of sub-image categories at the lower level, and repeating the training measure for each sub-image category at the lower level until a subdivision condition is satisfied, wherein the sub-image categories of all levels form a tree structure; loading the image under detection; performing a face recognition measure by comparing a similarity of each of the sub-image detectors at a same level as the image under detection according to the image under detection, and selecting the sub-image detector having a 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 sub-image detectors at a last level finish performing the face recognition measure on the image under detection. - View Dependent Claims (11, 12, 13, 14, 15)
-
Specification