Deception detection has been a hot research topic in many areas such as jurisprudence, law enforcement, business, and computer vision. However, there are still many problems that are worth more investigation. One of the major challenges is the data scarcity problem. So far, only one multi-modal benchmark dataset on deception detection has been published, which contains 121 video clips for deception detection (61 for deceptive class and 60 for truthful class). Therefore, most of the generated deception detection models (especially deep neural network-based methods) suffered from the overfitting problem and the bad generalization ability. To solve these problems, we proposed a novel Emotion Transformation Feature (ETF) to analyze deception detection with limited data. The critical analysis and comparison of the proposed methods with the state-of-the-art multi-modal methods have shown significant performance improvement up to 87.59%.