| dc.description.abstract | Nowadays, more and more people keep pets, as a result, more problems have happened, such as loss and abandonment, etc.…, causing a burden on society as well. Most of the existing identity recognition technologies are implanting the RFID chips. However, by this way, it will not only have the health concerns about pets, but also reduce the willingness of the owners to do so; besides, the chips implanted must be scanned by the instrument, and may face the failure of the implanted chips. The above reasons cause the difficulties in implementation of identity recognition.
In this study, a pet dog identity recognition system based on image processing is proposed, which uses dog nose texture features for identity recognition. The dog-nose area in the image is detected and segmented by the U-Net deep neural network. The segmented dog-nose area image combines three texture analysis algorithms such as LBP, GLCM, and GGCM for feature extraction, and the obtained Features are identified by PNN. We improved U-Net′s batch normalization strategy, which greatly improved the image segmentation performance of the nose pattern area, combined with the subsequent decision fusion PNN classifier, so that the accuracy of identity recognition reached excellent performance. In our self-built 10 pet dog database, our method can obtain 98% recognition rate. This study planned a complete pet identity recognition process, and used U-Net as the basis for image segmentation. With a small number of learning samples, it still has good cutting performance and provides correct and complete dog nose area for identity recognition. | en_US |