Locality constrained sparse representation for cat recognition

Yu Chen Chen, Shintami C. Hidayati, Wen-Huang Cheng, Min Chun Hu, Kai Lung Hua*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


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.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings
EditorsRichang Hong, Nicu Sebe, Qi Tian, Guo-Jun Qi, Benoit Huet, Xueliang Liu
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319276731
StatePublished - 1 Jan 2016
Event22nd International Conference on MultiMedia Modeling, MMM 2016 - Miami, United States
Duration: 4 Jan 20166 Jan 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on MultiMedia Modeling, MMM 2016
CountryUnited States


  • Biometrics
  • Cat recognition
  • Data locality
  • Dictionary learning
  • Sparse representation

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