METHOD OF EXTRACTING FEATURE OF INPUT IMAGE BASED ON EXAMPLE PYRAMID, AND FACIAL RECOGNITION APPARATUS

• US 20160078283A1
• Filed: 08/04/2015
• Published: 03/17/2016
• Est. Priority Date: 09/16/2014
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Hwang, Wonjun, KIM, Wonjun, Suh, Sungjoo, Kim, Jungbae, HAN, JaeJoon
• Granted Patent: US 9,875,397 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/21345 enforcing sparsity or involving a domain transformation
• CPC Class Codes: G06F 18/24133 Distances to prototypes
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

At least one example embodiment discloses a method of extracting a feature of an input image. The method includes constructing an example pyramid including at least one hierarchical level based on stored example images, generating a codebook in each of the at least one hierarchical level, calculating a similarity between the codebook and the input image, and extracting a feature of the input image based on the similarity.

Claims

1. A method of extracting a feature of an input image, the method comprising:

constructing a example pyramid including at least one hierarchical level based on stored example images;

generating a codebook in each of the at least one hierarchical level;

calculating a similarity between the codebook and the input image; and

extracting a feature of the input image based on the similarity.

2. The method of claim 1, wherein the constructing comprises:

generating example image groups for each of the at least one hierarchical level by clustering the example images based on a reference; and

constructing the example pyramid based on the example image groups.

3. The method of claim 2, wherein the generating the example image groups comprises:

projecting a feature vector of the example images to a feature space; and

generating the example image groups for each of the at least one hierarchical level by clustering the feature vector based on a distance in the feature space.

4. The method of claim 1, wherein the generating the codebook comprises:

generating a plurality of visual words based on example image groups in each of the at least one hierarchical level; and

generating the codebook based on the visual words.

5. The method of claim 4, wherein the generating the visual words comprises:

performing vector quantization on the example image groups in each of the at least one hierarchical level; and

generating the visual words based on the vector quantization.

6. The method of claim 4, wherein the generating the visual words comprises:

performing sparse coding on the example image groups in each of the at least one hierarchical level; and

generating the visual words based on the sparse coding.

7. The method of claim 1, wherein the calculating comprises:

measuring distances between the input image and a plurality of groups associated with visual words in the codebook in a feature space; and

calculating the similarity based on the measured distances.

8. The method of claim 7, further comprising:

concatenating the measured distances.

9. The method of claim 1, wherein the extracting comprises:

extracting the feature of the input image based on a distribution of a probability value with respect to the similarity.

10. The method of claim 9, wherein the extracting comprises:

assigning a weight to the distribution of the probability value; and

extracting the feature of the input image based on the weight.

11. A non-transitory computer-readable medium comprising program code that, when executed by a processor, causes the processor to perform the method of claim 1.

12. A facial recognition apparatus comprising:

a face extractor configured to extract a facial area from an input image;

a normalizer configured to perform normalization on the facial area;

a feature extractor configured to extract a feature of the input image based on the normalized facial area and a stored codebook; and

a recognizer configured to recognize a face based on the extracted feature,

wherein the codebook is based on example image groups in each level of a example pyramid using stored example images.

13. The apparatus of claim 12, wherein the feature extractor is configured to calculate a similarity between the normalized facial area and the codebook, and extract the feature of the input image based on the similarity.

14. The apparatus of claim 13, wherein the feature extractor is configured to measure distances between the normalized facial area and a plurality of groups associated with visual words in the codebook in a feature space, and calculate the similarity based on the measured distances.

15. The apparatus of claim 14, wherein the feature extractor is configured to assign a weight to a distribution of a probability value with respect to the similarity, and extract the feature of the input image based on the weight.

16. The apparatus of claim 12, wherein the recognizer is configured to recognize a face using a classifier based on the extracted feature.

17. The apparatus of claim 12, further comprising:

a codebook generator configured to construct the example pyramid including at least one hierarchical level based on the stored example images, and generate the codebook for example image groups in each of the at least one hierarchical level.

18. The apparatus of claim 17, wherein the codebook generator is configured to project a feature vector of the stored example images onto a feature space, generate the example image groups for each of the at least one hierarchical level by clustering the feature vector based on a distance in the feature space, and construct the example pyramid based on the example image groups.

19. The apparatus of claim 17, wherein the codebook generator is configured to generate a plurality of visual words based on the example image groups and generate the codebook based on the visual words.

20. The apparatus of claim 19, wherein the codebook generator is configured to perform vector quantization on the example image groups and generate the visual words based on the vector quantization.