论文标题
评估新颖的面膜架构，以进行耳罩细分
Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation
论文作者
论文摘要
人耳通常是普遍的，可收藏的，独特的和永久的。基于耳朵的生物识别识别是正在探索的利基市场和最新方法。为了使任何基于EAR的生物识别算法的性能良好，需要准确执行耳朵检测和分割。尽管在现有文献上为边界框做了重大工作，但缺乏方法输出了耳朵的分割掩码。本文将三个较新的型号与跨四个不同数据集的最新模型（Resnet 101 +FPN）模型进行了训练和比较。报告的平均精度（AP）分数表明，较新的模型的表现优于最先进的模型，但没有人在多个数据集上表现最好。
The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.
