Face recognition by using ring rotation invariant transform and coping with image rotation and image shifting problems

Ching-Liang Su

doi:10.1117/12.707236

17 February 2007 Face recognition by using ring rotation invariant transform and coping with image rotation and image shifting problems

Ching-Liang Su

Author Affiliations +

Proceedings Volume 6503, Machine Vision Applications in Industrial Inspection XV; 65030H (2007) https://doi.org/10.1117/12.707236
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Generally, for the face image recognition, we must cope with the image shift and image rotation problem. To cope with the image-shifting problem, this research uses one pixel inside the sample image to compare with the around pixels that surrounding the corresponding pixel that inside the unknown image. The "ring rotation invariant transform" technique is used to transfer the geometry feature of the face image to another more salient feature. By this approaching one can obtain more salient geometry feature of the face image. By this more salient geometry feature, one can judge whether or not the sample image and the unknown image are the same image. The "ring rotation invariant transform" technique can solve the image rotation problem. In this research, three different kinds of extracted ring signals are generated. The extracted ring signals are generated by the following ring-circles - ring-radius-31-circle, ring-radius-22-circle, and ringradius- 13-circle. These extracted ring-signals are used to generate the rotation invariant vector magnitude quantities. These rotation invariant vector magnitude quantities are combined as one entity and this entity is saved inside one specific corresponding pixel in the BMP file. By this approach, one pixel will possess more geometry-features of the face images. The obtained entity of the combined signals of one specific pixel inside the sample image will be compared to the entities of the combined signals of the entire pixels located in the corresponding radius-6-cake in the unknown image. By this comparison, one can find the most-matching point of the geometry-feature of the pixels between the sample image and the unknown image.

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Ching-Liang Su "Face recognition by using ring rotation invariant transform and coping with image rotation and image shifting problems", Proc. SPIE 6503, Machine Vision Applications in Industrial Inspection XV, 65030H (17 February 2007); https://doi.org/10.1117/12.707236

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KEYWORDS

Signal generators

Facial recognition systems

Algorithm development

Databases

Image processing

Analytical research

Machine vision

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