| dc.description.abstract | At present, the government mainly uses embedded RFID chip to supervise pets and stray animals to identify the pets. As the means is invasive, common people′ willingness to embed the chip in pets is not high, leading to administrative vulnerability. This study proposes a multi modal biological recognition method for using non-invasive image recognition method to identify pets. The dog species is classified by CNN (Convolutional Neural Network), and then the pet′s multi modal biological features are extracted, such as muzzle pattern, body contour and facial geometry. Finally, the dog is identified by hybrid neural network classifier. A Multi Modal Hybrid Neural Network (MM-HNN) classifier system is designed to validate the performance of pet identification. The empirical comparison shows that the Equal Error Rate (EER) of simply using CNN deep learning model for pet identification is 23.77%, the EER of MM-HNN pet identification is 13.45%, and the EER of CNN dog species recognition + MM-HNN identification is 4.65%. In the experiment on three groups of fellow dog, the EER of identification is 4.57%. In the recognition experiment on three biological feature modals, the texture modal recognition rate is 88.33%, the contour modal recognition rate is 84.83%, the face mode recognition rate is 79.83%, and the multi modal recognition rate of the three modals is 95%, proving that the MM-HNN classifier has good recognition performance. | en_US |