A novel technique for texture description and image classification based in RGB compositions

At present, facial recognition entertains great importance in performing authentication processes, because it prevents unauthorized access to devices and places. Additionally, it allows for the identification of persons. Henceforth, this paper proposes a novel texture descriptor called Cyclical Chroma and a new classification technique, which takes in consideration the sub-pixel values of 0-255 for each RGB (Red, Green, Blue) channel that conforms the image. To verify the effectiveness of the proposed techniques, tests were performed with a database of images in a controlled environment and in one under uncontrolled conditions; additionally, Cyclical Chroma was tested with a different classifier, denominated the Multiclass Classifier, and the results were compared against other descriptors, including GLCM, SHDH, LQP, and CCR, demonstrating the effectiveness of the proposed techniques with 100% efficiency with controlled images and 78% effectiveness under uncontrolled conditions prior to the application of an equalization technique, increasing the efficiency to 100%.