UASSIGNED:虹膜模式识别系统在人类中得到了很好的发展和实践,然而,在野外条件下,关于虹膜识别系统在动物中应用的信息很少,主要挑战是即使在适当约束的情况下,也要从不断移动的非合作动物中捕获高质量的虹膜图像。该研究的目的是通过生物识别来验证和识别黑孟加拉山羊,以改善其可追溯性系统中的动物管理。
未经批准:四十九健康,无疾病,在农民田地随机选择3个月±6日龄的雌性黑孟加拉山羊。使用为人类虹膜扫描仪制造的专用相机从3、6、9和12月龄的单个山羊的左眼捕获眼睛图像。iGoat软件用于匹配3、6、9和12月龄的相同个体山羊。Resnet152V2深度学习算法进一步应用于相同的图像集以仅使用捕获的眼睛图像而不提取其虹膜特征来预测匹配百分比。
未经评估:在山羊内部和之间计算的匹配阈值为55%。3、6、9、12月龄山羊模板匹配准确率为81.63%,90.24%,44.44%和16.66%,分别。由于匹配9个月和12个月大的山羊的准确性较低,并且低于最小阈值匹配百分比,此虹膜模式匹配过程不可接受.resnet152V2深度学习模型的验证准确率为82.49%,92.68%,3、6、9、12月龄山羊的鉴定率分别为77.17%和87.76%,分别训练后的模型。
UNASSIGNED:这项研究强烈支持使用眼睛图像的深度学习方法可以用作单个山羊的生物特征识别的签名。
OBJECTIVE: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system.
METHODS: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer\'s field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features.
RESULTS: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model.
CONCLUSIONS: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.