Department of Computer Science, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
Sensors (Basel). 2020 Jun 17;20(12):3419. doi: 10.3390/s20123419.
It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme.
最近的再识别（Re-ID）工作表明，即使在更换服装后，人体的全身图像也能揭示其体型。这种生物特征的一个显著优势是，即使在远处，也可以很容易地通过标准的 2D 相机拍摄到人体的全身图像来进行捕捉。在这项工作中，基于全身图像的体型被研究为一种新颖的软生物特征，用于远距离和移动中的人员识别。研究和评估了两种常见的场景：（i）识别和（ii）验证。为此，招募了两个不同的深度网络，一个用于识别场景，一个用于验证场景。实验在流行的、公开可用的数据集上进行，结果表明，由于易于获得所需的图像，体型确实可以成为远距离和移动中身份识别的有价值的生物特征（因此也适用于非合作的个体）。这种软生物特征在更广泛的生物特征融合方案中尤其有用。
