Method for processing digital image information from photographic images

• US 20030198367A1
• Filed: 03/24/2003
• Published: 10/23/2003
• Est. Priority Date: 04/22/2002
• Status: Abandoned Application

• Current Assignee: Agfa-Gevaert Aketiengsellschaft, Agfa-Gevaert AG (Agfa-Gevaert N.V.)
• Original Assignee: Agfa-Gevaert AG (Agfa-Gevaert N.V.)
• Inventors: Keupp, Wolfgang, Forrester, Ruth, Hartmann, Klaus-Peter
• Application Number:
  US10/396,217
• Publication Number:
  US 20030198367A1
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/003 Deblurring; Sharpening vibration or motion blur correction for cameras comprising an electronic image sensor H04N23/682
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions

Abstract

An automatic method for processing digital image data from photographic images, particularly of photographs containing people and portraits, includes a face-specific image processing that distinguishes itself from that used to process other images. Face-specific image processing is automatically selected when faces are identified within the image data by means of an object recognition procedure.

Claims

1. Method for processing digital image data of photographic images, whereby image data from the photograph of a person are processed by means of face-specific image processing that distinguishes itself from processing used to process other image data, the improvement comprising the steps of implementing an automatic object-recognition algorithm to identify the presence of human faces in the image data and automatically selecting face-specific image processing when faces are identified within the image data.

2. Method as in claim 1, wherein the object recognition algorithm identifies faces based on gray-scale image density progressions in image data typical of faces.

3. Method as in claim 1, wherein the object recognition algorithm is applied to a reduced-resolution image data set in order to save computer time.

4. Method as in claim 1, wherein the object recognition algorithm includes the step of comparing the image data to a plurality of templates.

5. Method as in claim 1, wherein the object recognition algorithm includes the step of using deformable grids.

6. Method as in claim 1, wherein the object recognition algorithm is applied only to pre-selected images.

7. Method as in claim 1, wherein the focus of face image data is softened during face-specific image processing.

8. Method as in claim 7, wherein the focus in skin-tone areas of the face is softened.

9. Method as in claim 7, wherein the focus is softened in areas of the face in which there are no abrupt changes in density.

10. Method as in claim 1, wherein at least one of the red component of the image data is increased and the blue component is decreased during face-specific image processing in order to create a warmer impression of the image.

11. Method as in claim 10, wherein the color component of skin-tone areas of the face is altered.

12. Method as in claim 1, wherein density variations of image data within skin-tone areas of the face are made glossy during face-specific image processing.

13. Method as in claim 1, wherein face-specific image processing is automatically selected when the size of the identified face comprises more than 10% of the overall image area.

14. Method as in claim 1, further comprising the step of automatically selecting one of a plurality of face-specific image-processing options are provided whose selection occurs automatically based on the classification of photographs containing people and in dependence upon the face size.

15. Method as in claim 14, wherein a photograph containing people is classified as an entire-person photograph if the face size comprises less than 10% of the overall image area.

16. Method as in claim 14, wherein a photograph containing people is classified as a portrait photograph if the face size comprises more than 10% of the overall image area.

17. Method as in claim 14, wherein the photograph containing people is classified as a large-portrait photograph if the face size comprises more than 20% of the overall image area.

18. Method as in claim 17, further comprising the step of altering the face position in a large-portrait photograph.

19. Method as in claim 17, wherein a section of the image is reformatted to the overall image size for a large-portrait photograph such that the face and the background are in a ratio to each other ideal for a portrait photograph.

20. Method as in claim 16, wherein the focus of the overall image data, or of image data of portions of a face, is less strongly sharpened or is unsharpened for a portrait photograph.

21. Method as in claim 16, wherein the focus for the eyes is sharpened for a portrait photograph in order to give the photograph a sharp and bright appearance.

22. Method as in claim 16, wherein the focus of the hair is sharpened for a portrait photograph.

23. Method as in claim 16, wherein extremely light areas within faces are darkened for a portrait photograph.

24. Method as in claim 16, wherein the focus of the face surroundings is increasingly softened toward the edge of the image so that the background is not distracting.

25. Method as in claim 16, wherein the focus of the region of the image area immediately surrounding the face is sharpened for a portrait photograph.