LemurFaceID: a face recognition system to facilitate individual identification of lemurs
LemurFaceID: a face recognition system to facilitate individual identification of lemurs
复制标题
DOI：
10.1186/s40850-016-0011-9
复制
发表时间：
2017-02-17
期刊：
影响因子：
1.6
通讯作者：
Tecot, Stacey R.
中科院分区：
文献类型：
--
作者：
Crouse, David;Jacobs, Rachel L.;Tecot, Stacey R.
背景资料：对已知个体的长期研究对于理解影响自然种群的人口统计和进化过程至关重要。目前用于许多动物的个体识别的方法包括捕获和标记技术和/或个体表型中的自然变异的研究人员知识。这些方法可能是昂贵的，耗时的，并且对于更大规模的人群水平的研究可能是不切实际的。因此，对于许多动物谱系，长期研究项目往往仅限于少数类群。狐猴是马达加斯加特有的哺乳动物谱系，也不例外。许多物种缺乏解决进化问题所需的长期数据。这至少部分是由于难以长期收集已知个人的一致数据。在这里，我们提出了一种新的方法来识别狐猴的个体（LemurFaceID）。LemurFaceID是一个计算机辅助的面部识别系统，可用于识别个人的狐猴photographs.Results的基础上：LemurFaceID开发使用补丁明智的多尺度本地二进制模式功能和修改后的面部图像归一化技术，以减少面部毛发和环境照明的变化对识别的影响。我们训练和测试我们的系统使用的图像从野生红腹狐猴（Eulemur rubriventer）在Ranomafana国家公园，马达加斯加。在100次试验中，不同的分区的训练和测试集，我们证明了LemurFaceID可以达到98.7% +/- 1.81%的准确率（使用2查询图像融合）在正确识别个别lemurs.Conclusions：我们的研究结果表明，人类面部识别技术可以修改为识别个别狐猴的基础上，在面部模式的变化。LemurFaceID能够根据野生个体的照片以相对较高的准确度识别个体狐猴。这项技术将消除传统个人身份识别方法的许多局限性。一旦优化，我们的系统可以通过提供快速，具有成本效益和准确的个体识别方法来促进对已知个体的长期研究。
Background: Long-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, population-level studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs.Results: LemurFaceID was developed using patch-wise Multiscale Local Binary Pattern features and modified facial image normalization techniques to reduce the effects of facial hair and variation in ambient lighting on identification. We trained and tested our system using images from wild red-bellied lemurs (Eulemur rubriventer) collected in Ranomafana National Park, Madagascar. Across 100 trials, with different partitions of training and test sets, we demonstrate that the LemurFaceID can achieve 98.7% +/- 1.81% accuracy (using 2-query image fusion) in correctly identifying individual lemurs.Conclusions: Our results suggest that human facial recognition techniques can be modified for identification of individual lemurs based on variation in facial patterns. LemurFaceID was able to identify individual lemurs based on photographs of wild individuals with a relatively high degree of accuracy. This technology would remove many limitations of traditional methods for individual identification. Once optimized, our system can facilitate long-term research of known individuals by providing a rapid, cost-effective, and accurate method for individual identification.