Click on “Download PDF” for the PDF version or on the title for the HTML version.
If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.
Cat face recognition using deep learning
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800316.(doi:10.13031/aim.201800316)
Authors: Tzu-Yuan Lin, Yan-Fu Kuo
Keywords: Convolutional neural network, cat, face recognition, support vector machine.
Abstract. In Taiwan, there are more than 3 thousand cats and dogs missing every year. Losing pets could be extremely painful for owners. It also places burden on animal shelters in trying to return the pets to the owners. Although implanting microchips has always been a way to solve the missing pet problem, it may cause health problems (e.g., inflammatory reaction and cancer) to animals. Hence, a noninvasive approach for identifying missing pets is needed. This work proposed to identify cats noninvasively using face recognition. A database that contains 1500 images of 150 different cats was developed. Facial parts (e.g., eyes, nose, and mouth) were identified using convolutional neural networks. The features of the facial parts (e.g., eigenface) were then qualified and were used for identifying the cats with support vector machines (SVMs). The proposed method achieves an identification accuracy of 94.1%.
(Download PDF) (Export to EndNotes)