Recently, human gait pattern has turned into an essential biometric feature to recognize an individual remotely. Gait as a feature becomes challenging owing to variation in appearance under different covariate conditions (eg, shoe, surface, haul, viewpoint and attire). The covariates may alter few fragment of gait while other fragment stay unaltered, leading to lower the probability of correct identification. To overcome such variation, an improved gait recognition strategy is proposed in this article by gait energy image partitioning and selection processing. Our method involves pre‐processing of raw video for silhouette extraction, gait cycle detection, segmentation into different regions, and histogram of gradients feature extraction from selected segments. In this way, the specific features across complete gait cycles are extracted precisely. Finally, recognition is done by using K‐NN. The proposed strategy has been assessed using the CASIA B gait database. Our outcomes shows a particular proposed strategy accomplishes high recognition rate and outperforms the advanced gait recognition mechanism.

中文翻译：

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通过步态能量图像分割改善步态识别

最近，人的步态模式已成为一种基本的生物特征，可以远程识别一个人。由于在不同的协变量条件下（例如，鞋子，地面，拖拉，视点和着装）的外观变化，步态成为一项挑战。协变量可能会改变步态的几个片段，而其他片段则保持不变，从而降低正确识别的可能性。为了克服这种变化，本文提出了一种通过步态能量图像分割和选择处理的改进的步态识别策略。我们的方法包括对原始视频进行预处理，以进行轮廓提取，步态周期检测，分割为不同区域以及从所选片段中提取梯度特征的直方图。这样，可以精确提取整个步态周期中的特定特征。最后，使用K‐NN进行识别。拟议的策略已使用CASIA B步态数据库进行了评估。我们的结果表明，提出的特定策略可以实现较高的识别率，并且优于先进的步态识别机制。