Method of multi-level facial image recognition and system using the same
First Claim
Patent Images
1. A method of multi-level facial image recognition, comprising the steps of:
- (A) inputting an original image of a face;
(B) performing a pre-process to trim the original image into a facial image only containing a complete face image;
(C) decomposing the facial image into N resolutions, each having M channels, where N≧
2 and M≧
2, so that the facial image is decomposed into N×
M sub-images;
(D) in a learning stage, using a front facial image with a normal expression as a learning image;
inputting sub-images decomposed from the learning image to N×
M self-organizing map neural networks, respectively, for performing a non-supervisory classification learning;
when the neural networks complete a predetermined learning process, the sub-images of the learning image being input to M neural networks that has completed the learning again, so that each neural network generates a winning unit; and
(E) in a testing stage, decomposing a test image thereby starting from the M sub-images having a lowest resolution and inputting the sub-images into the corresponding self-organized map neural networks for generating M winning units;
performing a recognition decision process for determining distances from the M winning units to the winning units of each learning image in a corresponding self-organizing map neural network thereby finding possible candidates, and if there is only one candidate, the candidate being a winner and the decision process being completed, while there are more than one candidates, the candidates being retained for performing a decision process in a relative high level of resolution.
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Abstract
A quadrature mirror filter is applied to decompose an image into at least two sub-images each having a different resolution. These decomposed sub-images pass through self-organizing map neural networks for performing a non-supervisory classification learning. In a testing stage, the recognition process is performed from sub-images having a lower resolution. If the image can not be identified in this low resolution, the possible candidates are further recognized in a higher level of resolution.
167 Citations
8 Claims
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1. A method of multi-level facial image recognition, comprising the steps of:
-
(A) inputting an original image of a face;
(B) performing a pre-process to trim the original image into a facial image only containing a complete face image;
(C) decomposing the facial image into N resolutions, each having M channels, where N≧
2 and M≧
2, so that the facial image is decomposed into N×
M sub-images;
(D) in a learning stage, using a front facial image with a normal expression as a learning image;
inputting sub-images decomposed from the learning image to N×
M self-organizing map neural networks, respectively, for performing a non-supervisory classification learning;
when the neural networks complete a predetermined learning process, the sub-images of the learning image being input to M neural networks that has completed the learning again, so that each neural network generates a winning unit; and
(E) in a testing stage, decomposing a test image thereby starting from the M sub-images having a lowest resolution and inputting the sub-images into the corresponding self-organized map neural networks for generating M winning units;
performing a recognition decision process for determining distances from the M winning units to the winning units of each learning image in a corresponding self-organizing map neural network thereby finding possible candidates, and if there is only one candidate, the candidate being a winner and the decision process being completed, while there are more than one candidates, the candidates being retained for performing a decision process in a relative high level of resolution.- View Dependent Claims (2, 3, 4)
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5. A multi-level facial image recognition system comprising:
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means for inputting an original image of a face;
means for performing a pre-process to trim the original image into a facial image only containing a complete face image;
means for decomposing the facial image into N resolutions, each having M channels, where N≧
2 and M≧
2, so that the facial image is decomposed into N×
M sub-images; and
a plurality of self-organizing map neural networks, wherein, in a learning stage, a front facial image with a normal expression is used as a learning image;
the sub-images decomposed from the learning image are input to N×
M self-organizing map neural networks, respectively, for performing a non-supervisory classification learning;
when the neural networks complete a predetermined learning process, the sub-images of the learning image are input to M neural networks that has completed the learning again, so that each neural network generates a winning unit; and
wherein, in a testing stage, a test image is decomposed thereby starting from the M sub-images having a lowest resolution and inputting the sub-images into the corresponding self-organized map neural networks for generating M winning units;
a recognition decision process is performed for determining distances from the M winning units to the winning units of each learning image in a corresponding self-organized map neural network thereby finding possible candidates, and if there is only one candidate, the candidate is a winner and the decision process is completed, while there are more than one candidates, the candidates are retained for performing a decision process in a relative high level of resolution.- View Dependent Claims (6, 7, 8)
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Current AssigneeInstitute For Information Industry
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Original AssigneeInstitute For Information Industry
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InventorsTsai, Kun-Cheng
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Primary Examiner(s)Wu, Jingge
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Assistant Examiner(s)Hesseltine, Ryan J.
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Application NumberUS09/736,264Publication NumberTime in Patent Office1,166 DaysField of Search382/115, 382/116, 382/118, 382/119, 382/124, 382/155, 382/156, 382/157, 382/158, 382/159, 340/5.53, 340/5.83, 706/15, 706/16, 706/20, 706/22, 706/25, 375/240.19US Class Current382/118CPC Class CodesG06V 30/2504 Coarse or fine approaches, ...G06V 40/172 Classification, e.g. identi...
