![rotate image multispec rotate image multispec](https://landing-assets.pics.io/img/raw/images/ToolRotatePurpose@2x.jpg)
G. Lua, D. Zhang and K. Wanga, Pattern Recogn.to the definition of e4n (10), but with the aforementioned axis rotation. R. Brunelli and T. Poggio, IEEE Trans.It adjusts the size of the image accordingly while rotating the image. Rotatebound: it overcomes the problem happened with rotate. however the drawback is image might get cropped if it is not a square image. By this approach, one pixel will possess more geometrical-features of the face images one entity in sample image is compared with entities inside the corresponding radius-6-cake area of the unknown image to locate the closest matching point. rotate: Rotate the image at specified angle. These signals are used to generate the rotation invariant magnitudes and several magnitudes are combined as one entity and, subsequently, saved inside one specific corresponding pixel in the BMP file. In this study, three different kinds of extracted ring signals are generated, which are (1) ring-radius-31, (2) ring-radius-22, and (3) ring-radius-13. To deal with the image-shifting problem, this study uses one pixel inside a sample image to compare with the corresponding pixels in the unknown image to locate the closest matching point. It also can solve image rotation problem.
![rotate image multispec rotate image multispec](https://www.ijser.org/paper/AFIM-A-High-Level-Conceptual-ATM-Design-Using-Composite/Image_012.png)
The "ring rotation invariant transform" technique is used to transfer geometrical features of face image to other more salient ones by which one can identify whether a sample or unknown image is the identical image. Generally, for face recognition, image shift and rotation problems must be addressed.