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Cat face recognition using deep learning

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

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%.

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