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Gait biometrics via optical flow motion features for people identification

机译：通过光流运动功能进行步态生物识别以识别人

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Numerous published studies have confirmed the potentials of deploying gait as biometrics within forensic and surveillance scenarios. Few of these have addressed the contribution of motion-based features on the recognition process. We describe in this paper a descriptor based on computing the optical flow of consecutive frames to generative a discriminative biometric signature for gait recognition. A set of experiments are carried out using the CASIA dataset to explore the usefulness of motion-based features for gait identification subjected to different covariate factors including clothing and carrying conditions. Based on a limited dataset containing 100 samples, higher recognition rates are achieved using plain motion features using the nearest neighbor classifier. The attained results affirm that people identification via the use of features from gait kinematics is achievable with acceptable success rates regardless of the covariate factors. This is a crucial milestone in shifting academic studies on gait biometrics from research settings to forensic and surveillance environments.
机译：大量已发表的研究证实了在法医和监视场景中将步态作为生物识别技术使用的潜力。其中很少有人解决基于运动的特征对识别过程的贡献。我们在本文中描述了一个描述符，该描述符基于计算连续帧的光流以生成区分生物特征的步态识别特征。使用CASIA数据集进行了一组实验，以探索基于运动的特征对受不同协变量因素（包括衣物和携带条件）影响的步态识别的有用性。基于包含100个样本的有限数据集，使用最接近的邻居分类器使用普通运动特征可以实现更高的识别率。所获得的结果证实，无论协变量因素如何，通过步态运动学特征进行人识别都是可以达到可接受的成功率的。这是将步态生物特征的学术研究从研究环境转移到法医和监视环境的关键里程碑。

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