Natural vs artificial face classification using uniform local directional patterns and wavelet uniform local directional patterns
Face classification is a technique used in Biometrics to help distinguish between facial images. However, this technique has been applicable on human face images only. Online virtual worlds such as Second Life, Sims Online, etc. are gaining popularity over the Internet. They require human users to create a digital persona of oneself, known as an 'avatar'. Several avatars are designed to resemble human users. With crime being reported in virtual worlds, computer-generated avatar faces being created from human faces and human-resembling humanoids being designed, there is a need to distinguish between natural and artificial faces. Our work applies two new face classification techniques on grayscale, facial images of humans and avatars to tell them apart. (1) Uniform Local Directional Pattern (ULDP) utilizes the uniform patterns from Local Directional Pattern (LDP) (2) Wavelet Uniform Local Directional Pattern (WULDP) applies the ULDP technique on the wavelet transform of an image. Extensive experiments conducted on five different face image datasets (Caltech, FERET for human faces and Entropia, Second Life, Evolver for avatar faces) achieve baseline average classification accuracies of 98.55% using ULDP and 89.55% using WULDP respectively.