Vectorial Image Representation on the Texture Space (VIR-TS) Applied to RGB Image Classification: Face Recognition

In this paper, an RGB image with S is separated by its channels, obtaining an image in each color channel SR, SG and SB. The Vectorial Image Representation on the Texture Space (VIR-TS) transform is calculated for each channel; ergo, each image is represented with a given vector, SR→ C→R, SG→C→G and SB→C→B. Employing the C→R, C→G, and C→B vectors in a multi-class classifier, a database of RGB images was identified with the aim of verifying the classification efficiency of the VIR-TS transform. Based on the experimental results, the VIR-TS technique presents high efficiency when the noise is not added to the class and when the signal-to-noise ratio is high. For both instances, the efficiency presented is 100%. Nonetheless, if the class noise is high, the efficiency diminishes from 100%, to 95%, to 90% until it decreases to 10%. Based on the results obtained, the VIR-TS transform can be efficiently applied for the development of security systems and access control and can also be implemented in computer vision systems for medical diagnosis, drones, etc.