Face detection using templates

• US 5,835,616 A
• Filed: 06/17/1994
• Issued: 11/10/1998
• Est. Priority Date: 02/18/1994
• Status: Expired due to Term

• Current Assignee: University of Central Florida Research Foundation Inc. (State University System of Florida)
• Original Assignee: University of Central Florida (State University System of Florida)
• Inventors: Kwon, Young Ho, Lobo, Niels da Vitoria
• Primary Examiner(s): COUSO, YON JUNG
• Application Number:
  US08/261,947
• Time in Patent Office: 1,607 Days
• Field of Search: 382/115, 382/117, 382/118, 382/190, 382/199, 235/382, 235/380, 340/825.34
• US Class Current: 382/118
• CPC Class Codes: G06V 10/754 involving a deformation of the sample pattern or of the reference pattern; Elastic matching
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions
• CPC Class Codes: G06V 40/178 estimating age from face image; using age information for improving recognition

Abstract

Disclosed is a two step process for automatically finding a human face in an electronically digitized image (for example, taken by handheld digital cameras and digital video-cameras such as cam-corders), and for confirming the existence of the face by examining facial features. Step 1 is to detect the human face and is accomplished in stages that include enhancing the digital image with a blurring filter and edge enhancer in order to better set forth the unique facial features such as wrinkles, and curved shapes of a facial image. After prefiltering, preselected curves sometimes referred to as snakelets are dropped on the image where they become aligned to the natural wrinkles and curves of a facial image. Step 2 is to confirm the existence of the human face in seven stages by finding facial features of the digital image encompassing the chin, sides of the face, virtual top of the head, eyes, mouth and nose of the image. Ratios of the distances between these found facial features can be compared to previously stored reference ratios for recognition. This method for detecting facial features of an image can be used in applications such as but not limited to detecting human faces for the gathering of population age-statistics from patrons at entertainment/amusement parks and television network viewer-rating studies. Such gathering can include counting the patrons, distinguishing certain age and gender groups, and/or identifying specific people. Computer vision with this capability can further have application in such fields as automated surveillance systems, demographic studies, automated photography for point-and-shoot cameras and human computer interactions. Automated photography can eliminate the manual adjustment problems that result in poor quality from lack of focused subjects. Computer systems can utilize this system to recognize and respond to the specific needs of a user, and further translate for human users.

Claims

1. A method of detecting a single human face from a two dimensional digital image comprising the steps executed by a computer of:

(a) locating a single human face in a two-dimensional digital image by;

(i) blurring the digital image with a blurring filter;

(ii) enhancing edges of the digital image with an edge enhancer;

(iii) dropping first snakelet-curves onto the digital image , the first snakelet-curves using a Hough Transform voting process to find exterior oval-curves representing a single candidate face-location;

(iv) dropping second snakelet-curves inside the exterior oval-curves on the single candidate face-location, the second snakelet-curves using a Hough Transform voting process to determine positions of the chin and positions of sides of a single face; and

(b) confirming existence of additional facial features inside the exterior oval curves by;

(i) positioning a template outline on the chin positions and sides of the face positions to determine left and right eyes, mouth and nose of a face, the template outline having a single preselected configuration of a template left eye within a first preselected region, a template right eye within a second preselected region, a template mouth within a third preselected region and a template nose within a fourth preselected region;

(ii) determining positions of the left eye, the right eye, the nose, and the mouth with the template outline;

(iii) determining facial feature ratios between the left eye position, the right eye position, the nose position, the mouth position, the sides of face positions, and the chin position; and

(iv) verifying the facial feature ratios to be within selected value ranges.

2. The method of detecting the human face of claim 1, wherein the digital image is formed from:

a digital camera.

3. The method of detecting the human face of claim 1, wherein the digital image is formed from:

an analog signal converted to a digital image.