Vision-based pattern identification (such as face, fingerprint, iris etc.) has been successfully applied in human biometrics for a long history. However, dog nose-print authentication is a challenging problem since the lack of a large amount of labeled data. For that, this paper presents our proposed methods for dog nose-print authentication (Re-ID) task in CVPR 2022 pet biometric challenge. First, considering the problem that each class only with few samples in the training set, we propose an automatic offline data augmentation strategy. Then, for the difference in sample styles between the training and test datasets, we employ joint cross-entropy, triplet and pair-wise circle losses function for network optimization. Finally, with multiple models ensembled adopted, our methods achieve 86.67\% AUC on the test set. Codes are available at https://github.com/muzishen/Pet-ReID-IMAG.

翻译：长期以来,人类生物鉴别学成功地应用了基于愿景的模式识别(如脸部、指纹、iris等),然而,由于缺乏大量贴标签的数据,狗鼻印认证是一个具有挑战性的问题,为此,本文件介绍了我们在CVPR 2022生物鉴别技术(Re-ID)中提议的狗鼻印认证(Re-ID)任务的方法。首先,考虑到每个班级在训练组中只有少量样本的问题,我们提议了一个自动离线数据增强战略。随后,为了区分培训和测试数据集之间的样本样式,我们为网络优化采用了跨渗透、三重三重和对对对齐循环损失功能。最后,由于采用了多种模型,我们的方法在测试组上实现了86.67-AUC。 可在https://github.com/muzishen/Pet-ReID-IMAG上查阅代码。