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

• US 20050190953A1
• Filed: 10/19/2004
• Published: 09/01/2005
• Est. Priority Date: 10/28/2003
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Nagahashi, Toshinori, Hyuga, Takashi
• Application Number:
  US10/968,843
• Publication Number:
  US 20050190953A1
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 40/165 using facial parts and geometric relationships

Abstract

A method of the invention comprises the steps of sequentially selecting a predetermined area within the image G to be searched and then generating an image feature vector for the selection area, inputting the image feature vector into a support vector machine 30 which has learned beforehand the image feature vectors for a plurality of sample images for learning, and deciding whether or not a face image exists in the selection area based on a positional relation with a discrimination hyper-plane. Thereby, it is possible to search an area where a face image exists with high possibility from the image G to be searched at high speed and precisely.

Claims

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

sequentially selecting a predetermined area within said image to be searched and then generating an image feature vector for said selection area;

inputting said image feature vector into a support vector machine which has learned beforehand the image feature vectors for a plurality of sample images for learning; and

deciding whether or not a face image exists in said selection area based on a positional relation with a discrimination hyper-plane.

2. The face image candidate area searching method according to claim 1, wherein said image feature vector of said selection area is a non-face area partitioned by the discrimination hyper-plane for said support vector machine, and when the distance from said discrimination hyper-plane is greater than or equal to a predetermined threshold, it is decided that no face image exists near said selection image area.

3. The face image candidate area searching method according to claim 1, wherein a discriminant function of said support vector machine is a non-linear kernel function.

4. The face image candidate area searching method according to claim 1, wherein said image feature vector employs a corresponding value of each pixel reflecting a feature of face.

5. The face image candidate area searching method according to claim 1, wherein said image feature vector is generated employing the value regarding the intensity of edge in each pixel, the variance of edge in each pixel, or the value of brightness in each pixel, or a combination of those values.

6. The face image candidate area searching method according to claim 5, wherein said intensity of edge or said variance of edge in each pixel is generated employing a Sobel operator.

7. A face image candidate area searching system for searching an area considered to be face image where a face image exists with high possibility 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 selection area within said image to be searched and a sample image for learning;

a feature vector generation section for generating the image feature vectors of said selection area within said image to be searched and said sample image for learning that are read by said image reading section;

a support vector machine for acquiring a discrimination hyper-plane from the image feature vector of the sample image for learning that is generated by said feature vector generation section, and deciding whether or not a face image exists in said selection area based on a relation of the image feature vector of the selection area within said image to be searched that is generated by said feature vector generation section with said discrimination hyper-plane.

8. The face image candidate area searching system according to claim 7, wherein a discriminant function of said support vector machine is a non-linear kernel function.

9. A face image candidate area searching program for searching an area considered to be face image where a face image exists with high possibility 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 selection area within said image to be searched and a sample image for learning;

a feature vector generation step of generating the image feature vectors of said selection area within said image to be searched and said sample image for learning that are read at said image reading step;

a support vector machine for acquiring a discrimination hyper-plane from the image feature vector of the sample image for learning that is generated at said feature vector generation step, and deciding whether or not a face image exists in said selection area based on a relation of the image feature vector of the selection area within said image to be searched that is generated at said feature vector generation step with said discrimination hyper-plane.

10. The face image candidate area searching program according to claim 9, wherein a discriminant function of said support vector machine is a non-linear kernel function.