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.