METHOD AND APPARATUS OF IDENTIFYING USER USING FACE RECOGNITION

• US 20160026854A1
• Filed: 07/20/2015
• Published: 01/28/2016
• Est. Priority Date: 07/23/2014
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: HWANG, Wonjun, SUH, Sungjoo, KIM, Jungbae, HAN, Jaejoon
• Application Number:
  US14/803,332
• Publication Number:
  US 20160026854A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/23213 with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/24137 Distances to cluster centroïds
• CPC Class Codes: G06V 10/763 Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

At least one example embodiment discloses a user authentication method including acquiring representative reference images classified from a pre-stored first reference image of a user based on desired criteria, acquiring representative input images classified from a first input image based on the desired criteria, calculating a similarity between the first input image and the first reference image based on the representative input images and the representative reference images, and authenticating a user based on the calculated similarity.

Claims

1. A user authentication method comprising:

acquiring representative reference images classified from a first reference image of a user based on desired criteria;

acquiring representative input images classified from a first input image based on the desired criteria;

calculating a similarity between the first input image and the first reference image based on the representative input images and the representative reference images; and

authenticating a user based on the calculated similarity.

2. The method of claim 1, wherein the calculating calculates the similarity based on distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria.

3. The method of claim 1, wherein the calculating calculates the similarity based on a distance between a feature point of the first input image and a feature point of the first reference image and distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria.

4. The method of claim 1, wherein the calculating calculates the similarity based on a distance between a feature point of the first input image and a feature point of the first reference image, distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria, and a weight of each of the distances between the features points of the representative input images and the feature points of the representative reference images.

5. The method of claim 1, wherein the acquiring the representative reference images comprises:

classifying reference example sets from the first reference image; and

acquiring the representative reference images for each reference example set classified from the first reference image based on the desired criteria.

6. The method of claim 5, wherein the acquiring the representative reference images comprises:

classifying a plurality of reference example images similar to the first reference image into reference example sets based on the desired criteria through clustering; and

creating the representative reference images based on first reference example images similar to the first reference image that are retrieved from each reference example set.

7. The method of claim 6, wherein

the reference example images include at least one of example images acquired from different poses of the user and example images acquired based on different lighting brightness, and

the reference example images are stored in an example image database.

8. The method of claim 1, wherein the acquiring the representative input images comprises:

acquiring the representative input image for each input example set classified from the first input image based on the desired criteria.

9. The method of claim 1, wherein the acquiring of the representative input images comprises:

classifying a plurality of input example images similar to the first input image into n input example sets based on the desired criteria through clustering, n denoting a natural number greater than or equal to “

1”

; and

creating the n representative input images based on the input example images similar to the first input image that are retrieved from each reference example set.

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

calculating a similarity between each of the n input example sets and the first input image, and determining m input example images having the similarity greater than a reference value, m denoting a natural number greater than or equal to “

1”

; and

creating the n representative input images using the m input example images.

11. The method of claim 1, wherein the desired criteria comprises a variation in a pose of a face or a variation in a lighting.

12. A non-transitory computer-readable medium comprising a program for instructing a computer to perform the method of claim 1.

13. A user authentication apparatus comprising:

a storage configured to store a first reference image of a user;

a communicator configured to receive first input image; and

a processor configured to acquire representative reference images classified from the first reference image based on desired criteria, to acquire representative input images classified from the first input image based on the desired criteria, and to authenticate the user based on a similarity between the first input image and the first reference image that is based on the representative input images and the representative reference images.

14. The user authentication apparatus of claim 13, wherein the processor is configured to calculate the similarity based on distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria.

15. The user authentication apparatus of claim 13, wherein the processor is configured to calculate the similarity based on a distance between a feature point of the first input image and a feature point of the first reference image and distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria.

16. The user authentication apparatus of claim 13, wherein the processor is configured to calculate the similarity based on a distance between a feature point of the first input image and a feature point of the first reference image, distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria, and a weight of each of the distances between the features points of the representative input images and the feature points of the representative reference images.

17. The user authentication apparatus of claim 13, wherein the processor is configured to classify a plurality of reference example images similar to the first reference image into a plurality of reference example sets based on the desired criteria through clustering, and to create the representative reference images based on reference example images similar to the first reference image that are retrieved from each reference example set.

18. The user authentication apparatus of claim 17, wherein

the reference example images include at least one of example images acquired from different poses of the user and example images acquired based on different lighting brightness, and

the storage includes an example image database configured to store the reference example images.

19. The user authentication apparatus of claim 13, wherein the processor is configured to classify a plurality of input example images similar to the first input image into n input example sets based on the desired criteria through clustering, n denoting a natural number greater than or equal to “

1,” and

to create the n representative input images based on the input example images similar to the first input image that are retrieved from each reference example set.

20. A user recognition method comprising:

acquiring representative reference images classified from each of a plurality of first reference images of users based on desired criteria;

acquiring representative input images classified from a first input image based on the desired criteria;

calculating a similarity between the first input image and each of the first reference images based on the representative input images and the representative reference images; and

recognizing a user corresponding to the first input image from among the plurality of users based on the calculated similarity.

21. The method of claim 20, wherein the calculating calculates the similarity based on a distance between a feature point of the first input image and a feature point of each of the first reference images and distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria.

22. The method of claim 20, wherein the calculating calculates the similarity based on a distance between a feature point of the first input image and a feature point of each of the first reference images, distances between features points of the representative input images and feature points of the representative reference images that correspond to each other based on the desired criteria, and a weight of each of the distances between the features points of the representative input images and the feature points of the representative reference images.

23. The method of claim 20, wherein the acquiring the representative reference images comprises:

acquiring the representative reference images for each reference example set classified from each of the first reference images based on the desired criteria.

24. The method of claim 23, wherein the acquiring of the representative reference images comprises:

classifying a plurality of reference example images similar to each of the first reference images into a plurality of reference example sets based on the desired criteria through clustering; and

creating the representative reference images based on reference example images similar to each of the first reference images that are retrieved from each reference example set.

25. The method of claim 24, wherein

the reference example images include at least one of example images acquired from different poses of each of the users and example images acquired based on different lighting brightness, and

the reference example images are stored in an example image database.