The recent technological advances in surveillance, forensic and biometric systems to deter or even reduce the increasing number of crimes and prevent them is still questionable. The use of gait biometrics has attracted unprecedented interest due to its capability to work with low-resolution footage recorded from a distance. In contrast to mainstream research on gait biometrics which uses holistic silhouette features, the authors investigate the use of the bottom dynamic section within the human body to derive the most discriminative features for gait recognition. A new descriptor based on 7 Hu's moments is proposed describing the inner lower limb regions between the limbs being extracted only from landmark frames within one gait cycle. In order to assess the discriminatory potency of gait features from the lower regions for people identification, a number of experiments are conducted on the CASIA-B gait database to investigate the recognition rates using the KNN classifier and deep learning. The comparative analysis is performed against well-established research studies which were tested on the CASIA-B data set. The obtained results confirm the consistency of features extracted from the lower regions for gait recognition even under the impact of various factors.

中文翻译：

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步态生物识别技术：研究使用下部内部区域从地标框架识别人员的过程

监视，法医和生物识别系统在阻止，甚至减少犯罪数量和预防犯罪方面的最新技术进步仍然值得怀疑。步态生物识别技术的使用已经引起了空前的兴趣，这是因为它可以处理从远处记录的低分辨率素材。与使用整体轮廓特征的步态生物识别技术的主流研究相反，作者研究了利用人体底部的动态区域来得出最具区分性的步态识别特征。提出了一种基于7 Hu矩的新描述子，该描述子描述了仅从一个步态周期内的地标帧中提取的肢体之间的内部下肢区域。为了评估来自下部区域的步态特征的辨别力以进行人员识别，在CASIA-B步态数据库上进行了许多实验，以使用KNN分类器和深度学习研究识别率。对比分析是针对已在CASIA-B数据集上进行测试的公认研究进行的。获得的结果证实了即使在各种因素的影响下，从下部区域提取的用于步态识别的特征的一致性。