Face Identification Method and System Using Thereof

• US 20110150301A1
• Filed: 07/06/2010
• Published: 06/23/2011
• Est. Priority Date: 12/17/2009
• Status: Abandoned Application

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Han, Meng-Ju, Wang, Shih-Chieh, Song, Kai-Tai
• Application Number:
  US12/830,519
• Publication Number:
  US 20110150301A1
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A face identification method includes the following steps. First, first and second sets of hidden layer parameters, which respectively correspond to first and second database character vectors, are obtained by way of training according to multiple first and second training character data. Next, first and second back propagation neural networks (BPNNs) are established according to the first and second sets of hidden layer parameters, respectively. Then, to-be-identified data are provided to the first BPNN to find a first output character vector. Next, whether the first output character vector satisfies an identification criterion is determined. If not, the to-be-identified data are provided to the second BPNN to find a second output character vector. Then, whether the second output character vector satisfies the identification criterion is determined. If yes, the to-be-identified data are identified as corresponding to the second database character vector.

Claims

1. A face identification method for identifying to-be-identified data, which comprise an input character vector, the face identification method comprising the steps of:

respectively obtaining a first set of hidden layer parameters and a second set of hidden layer parameters by way of training according to a plurality of first training character data and a plurality of second training character data, which correspond to a first database character vector and a second database character vector, respectively;

establishing a first back propagation neural network (BPNN) and a second BPNN according to the first and second sets of hidden layer parameters, respectively;

providing the to-be-identified data to the first BPNN to find a first output character vector;

determining whether the first output character vector satisfies an identification criterion;

providing the to-be-identified data to the second BPNN to find a second output character vector when the first output character vector does not satisfy the identification criterion;

determining whether the second output character vector satisfies the identification criterion; and

identifying the to-be-identified data as corresponding to the second database character vector when the second output character vector satisfies the identification criterion.

2. The method according to claim 1, further comprising, after the step of determining whether the first output character vector satisfies the identification criterion, the step of:

identifying the to-be-identified data as corresponding to the first database character vector when the first output character vector satisfies the identification criterion.

3. The method according to claim 1, wherein the steps of obtaining the first and second sets of hidden layer parameters and establishing the first and second BPNNs respectively comprise:

obtaining a third set of hidden layer parameters corresponding to a third database character vector by way of training according to a plurality of third training character data; and

establishing a third BPNN according to the third set of hidden layer parameters.

4. The method according to claim 3, further comprising, after the step of determining whether the second output character vector satisfies the identification criterion, the steps of:

providing the to-be-identified data to the third BPNN to find a third output character vector when the second output character vector does not satisfy the identification criterion;

determining whether the third output character vector satisfies the identification criterion; and

identifying the to-be-identified data as corresponding to the third database character vector when the third output character vector satisfies the identification criterion.

5. The method according to claim 4, further comprising, after the step of determining whether the third output character vector satisfies the identification criterion, the step of:

identifying the to-be-identified data as corresponding to a character vector other than the first to third database character vectors when the third output character vector does not satisfy the identification criterion.

6. The method according to claim 1, further comprising the steps of:

selecting first face detection data from a first set of training image data and selecting second face detection data from a second set of training image data according to face skin color segmentation, morphology hole filling and attentional cascade; and

performing a dimensional simplification operation on the first face detection data and the second face detection data to obtain the first training character data and the second training character data according to the first and second face detection data, respectively.

7. The method according to claim 6, wherein the dimensional simplification operation is performed on the first and second sets of training character data by way of Karhunen-Loeve transformation.

8. A face identification system for identifying to-be-identified data, which comprise an input character vector, the face identification system comprising:

a face detection circuit for selecting first face detection data from a first set of training image data and selecting second face detection data from a second set of training image data;

a character analyzing circuit for performing a dimensional simplification operation on the first face detection data and the second face detection data to obtain a plurality of first training character data and a plurality of second training character data according to the first and second face detection data, respectively; and

an identification circuit, comprising;

a training module for obtaining a first set of hidden layer parameters and a second set of hidden layer parameters, respectively corresponding to a first database character vector and a second database character vector, by way of training according to the first training character data and the second training character data;

a simulating module for establishing a first back propagation neural network (BPNN) and a second BPNN according to the first and second sets of hidden layer parameters, respectively, and for inputting the to-be-identified data into the first BPNN to find a first output character vector; and

a control module for determining whether the first output character vector satisfies an identification criterion, wherein when the first output character vector does not satisfy the identification criterion, the control module controls the simulating module to provide the to-be-identified data to the second BPNN to find a second output character vector;

wherein the control module further determines whether the second output character vector satisfies the identification criterion, and when the second output character vector satisfies the identification criterion, the control module identifies the to-be-identified data as corresponding to the second database character vector.

9. The system according to claim 8, wherein when the first output character vector satisfies the identification criterion, the control module identifies the to-be-identified data as corresponding to the first database character vector.

10. The system according to claim 8, wherein:

the training module further obtains a third set of hidden layer parameters by way of training according to a plurality of third training character data, which correspond to a third database character vector; and

the simulating module further establishes a third BPNN according to the third set of hidden layer parameters.

11. The system according to claim 10, wherein:

when the second output character vector does not satisfy the identification criterion, the control module further determines whether a third output character vector satisfies the identification criterion; and

when the third output character vector satisfies the identification criterion, the control module identifies the to-be-identified data as corresponding to the third database character vector.

12. The system according to claim 11, wherein:

when the third output character vector does not satisfy the identification criterion, the control module identifies the to-be-identified data as corresponding to a character vector other than the first to third database character vectors.

13. The system according to claim 8, wherein the face detection circuit selects the first and second face detection data by way of face skin color segmentation, morphology hole filling and attentional cascade.

14. The system according to claim 8, wherein the character analyzing circuit performs the dimensional simplification operation on the first and second face detection data by way of Karhunen-Loeve transformation.