Method, system and program for searching area considered to be face image

• US 20050141766A1
• Filed: 10/14/2004
• Published: 06/30/2005
• Est. Priority Date: 10/15/2003
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Hyuga, Takashi, Nagahashi, Toshinori
• Application Number:
  US10/965,004
• Publication Number:
  US 20050141766A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/443 by matching or filtering
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 40/165 using facial parts and geometric relationships

Abstract

A sample image is filtered through a circumferential filter 28, an image feature amount is learned in a discrimination section 30, an image G to be searched is filtered through the circumferential filter 28 to detect a rotation invariant image feature amount for each filtered area, and each detected image feature amount is inputted into the discrimination section 30. Thereby, it is discriminated whether or not a filtering area is considered to be face image at high speed. And the dimensional number of image feature amounts is greatly reduced, so that not only the discrimination work but also the learning time of the sample image are greatly reduced.

Claims

1. A face image candidate area searching method for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said method comprising:

filtering each of a plurality of sample images for learning through a predetermined circumferential filter to detect each rotation invariant image feature amount, and learn said each image feature amount in a discrimination section;

sequentially filtering said image to be searched through the circumferential filter to detect a rotation invariant image feature amount for each filtered area;

sequentially inputting each detected image feature amount into said discrimination section; and

sequentially discriminating whether or not a filtering area corresponding to the image feature amount inputted using said discrimination section is considered to be face image.

2. The face image candidate area searching method according to claim 1, wherein said discrimination section employs a support vector machine or a neural network.

4. The face image candidate area searching method according to claim 1, wherein said rotation invariant image feature amount is any one of the intensity of edge, the variance of edge, or the brightness in each pixel, or a sum of the values of linearly integrating the average value of their combinations along the circumference of each circle for said circumferential filter for the number of circles.

5. The face image candidate area searching method according to claim 2, wherein said rotation invariant image feature amount is any one of the intensity of edge, the variance of edge, or the brightness in each pixel, or a sum of the values of linearly integrating the average value of their combinations along the circumference of each circle for said circumferential filter for the number of circles.

7. The face image candidate area searching method according to any one of claims 4, wherein the intensity of edge or the variance of edge in said each pixel is calculated using a Sobel operator.

13. The face image candidate area searching method according to any one of claims 5, wherein the intensity of edge or the variance of edge in said each pixel is calculated using a Sobel operator.

3. A face image candidate area searching method for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said method comprising:

filtering each of a plurality of sample images for learning through a predetermined circumferential filter to detect a rotation invariant image feature amount, and calculate an average face vector of said sample images from each image feature amount;

sequentially filtering said image to be searched through said circumferential filter to detect a rotation invariant image feature amount for each filtered area and calculate an image vector for each area from said image feature amount;

calculating the vector distance between each calculated image vector and said average face vector; and

sequentially discriminating whether or not an area corresponding to said image vector is considered to be face image depending on said calculated distance.

6. The face image candidate area searching method according to claim 3, wherein said rotation invariant image feature amount is any one of the intensity of edge, the variance of edge, or the brightness in each pixel, or a sum of the values of linearly integrating the average value of their combinations along the circumference of each circle for said circumferential filter for the number of circles.

14. The face image candidate area searching method according to any one of claims 6, wherein the intensity of edge or the variance of edge in said each pixel is calculated using a Sobel operator.

8. A face image candidate area searching system for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said system comprising:

an image reading section for reading a predetermined area within said image to be searched and a sample image for learning;

a feature amount calculation section for filtering the predetermined area within said image to be searched and said sample image for learning that are read by said image reading section through the same circumferential filters to calculate each rotation invariant image feature amount; and

a discrimination section for learning said rotation invariant image feature amount for said sample image for learning that is calculated by said feature amount calculation section and discriminating whether or not said predetermined area within said image to be searched calculated by said feature amount calculation section is considered to be face image from the learned results.

9. The face image candidate area searching system according to claim 8, wherein said discrimination section is a support vector machine or a neural network discriminator.

10. A face image candidate area searching system for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said system comprising:

an image reading section for reading a predetermined area within said image to be searched and a sample image for learning;

a feature amount calculation section for filtering the predetermined area within said image to be searched and said sample image for learning that are read by said image reading section through the same circumferential filters to calculate each rotation invariant image feature amount; and

a discrimination section for calculating an average face vector of said sample image for learning and an image vector of the predetermined area within said image to be searched from said rotation invariant image feature amounts calculated by said feature amount calculation section, and discriminating whether or not said predetermined area within said image to be searched is considered to be face image depending on the distance between both said calculated vectors by calculating the distance.

11. A face image candidate area searching program for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said program enabling a computer to perform:

an image reading step of reading a predetermined area within said image to be searched and a sample image for learning;

a feature amount calculation step of filtering the predetermined area within said image to be searched and said sample image for learning that are read at said image reading step through the same circumferential filters to calculate each rotation invariant image feature amount; and

a discrimination step of learning said rotation invariant image feature amount for said sample image for learning that is calculated at said feature amount calculation step and discriminating whether or not said predetermined area within said image to be searched calculated at said feature amount calculation step is considered to be face image from the learned results.

12. A face image candidate area searching program for searching an area considered to be face image having a high possibility where a face image exists from an image to be searched for which it is unknown whether or not any face image is contained, said program enabling a computer to perform:

an image reading step of reading a predetermined area within said image to be searched and a sample image for learning;

a feature amount calculation step of filtering the predetermined area within said image to be searched and said sample image for learning that are read by said image reading section through the same circumferential filters to calculate each rotation invariant image feature amount; and

a discrimination step of calculating an average face vector of said sample image for learning and an image vector of the predetermined area within said image to be searched from said rotation invariant image feature amounts calculated by said feature amount calculation section, and discriminating whether or not said predetermined area within said image to be searched is considered to be face image depending on the distance between both said calculated vectors by calculating the distance.