Locality constrained sparse representation for cat recognition

Cat (Felis catus) plays an important social role within our society and can provide considerable emotional support for their owners. Missing, swapping, theft, and false insurance claims of cat have become global problem throughout the world. Reliable cat identification is thus an essential factor in the effective management of the owned cat population. The traditional cat identification methods by permanent (e.g., tattoos, microchip, ear tips/notches, and freeze branding), semi-permanent (e.g., identification collars and ear tags), or temporary (e.g., paint/dye and radio transmitters) procedures are not robust to provide adequate level of security. Moreover, these methods might have adverse effects on the cats. Though the work on animal identification based on their phenotype appearance (face and coat patterns) has received much attention in recent years, however none of them specifically targets cat. In this paper, we therefore propose a novel biometrics method to recognize cat by exploiting their noses that are believed to be a unique identifier by cat professionals. As the pioneer of this research topic, we first collect a Cat Database that contains 700 cat nose images from 70 different cats. Based on this dataset, we design a representative dictionary with data locality constraint for cat identification. Experimental results well demonstrate the effectiveness of the proposed method compared to several state-of-the-art feature-based algorithms.