The ability to discern lies, more broadly known as deception detection, is an invaluable skill that can strongly influence the outcome of relevant situations such as court trials and police interrogatories. Several devices currently exist and are being used (e.g., magnetic resonance and polygraphs) to ease those tasks; although, due to the subject awareness of such tools, their effectiveness can be compromised by the person intentional behavioural changes. Thus, alternative ways to discriminate lies without using physical devices, could become critical assets for the aforementioned situations, especially in ever improving smart cities environments. In this letter, we present an unorthodox deception detection approach, based on hand gestures found in RGB videos of famous trials. The proposed system first extrapolates hands skeletons from the RGB sequences, then computes meaningful features which are summarized into Fisher Vectors (FVs), and finally feeds this representation to a Long-Short Term Memory (LSTM) network, defined Fisher-LSTM, to try and discern if a lie is being told. In the experimental results, we show how the FV representation can help a LSTM network grasp hand gestures characteristics that could otherwise be missed. What is more, the devised Fisher-LSTM, due to its real-time computation, can be employed in smart environments as an alternative lie detector in situations requiring an immediate response, such as the aforementioned law enforcement examples.

识别谎言的能力（更广泛地称为欺骗检测）是一项非常宝贵的技能，可以极大地影响有关情况（例如法院审判和警察审讯）的结果。当前存在几种设备，并且正在使用这些设备（例如磁共振和测谎仪）来简化这些任务；尽管由于对象对此类工具的了解，人们的故意行为改变可能会损害其有效性。因此，在不使用物理设备的情况下，辨别谎言的替代方法可能成为上述情况的关键资产，尤其是在不断改善的智慧城市环境中。在这封信中，我们基于著名试验的RGB视频中发现的手势，提出了一种非正统的欺骗检测方法。拟议的系统首先从RGB序列中推断出手的骨骼，然后计算出有意义的特征，这些特征被汇总到Fisher向量（FV）中，最后将这种表示形式提供给定义为Fisher-LSTM的长短期记忆（LSTM）网络，以进行尝试并辨别是否在说谎。在实验结果中，我们展示了FV表示如何帮助LSTM网络掌握否则可能会遗漏的手势特征。而且，由于其实时计算，设计的Fisher-LSTM可以在智能环境中用作需要立即响应的情况下的替代测谎器，例如上述执法示例。最后将此表示提供给定义为Fisher-LSTM的长期记忆（LSTM）网络，以尝试辨别是否在说谎。在实验结果中，我们展示了FV表示如何帮助LSTM网络掌握否则可能会遗漏的手势特征。而且，由于其实时计算，设计的Fisher-LSTM可以在智能环境中用作需要立即响应的情况下的替代测谎器，例如上述执法示例。最后将此表示提供给定义为Fisher-LSTM的长期记忆（LSTM）网络，以尝试辨别是否在说谎。在实验结果中，我们展示了FV表示如何帮助LSTM网络掌握否则可能会遗漏的手势特征。而且，由于其实时计算，设计的Fisher-LSTM可以在智能环境中用作需要立即响应的情况下的替代测谎器，例如上述执法示例。