  With the exponential increase in video content, the need for accurate
deception detection in human-centric video analysis has become paramount. This
research focuses on the extraction and combination of various features to
enhance the accuracy of deception detection models. By systematically
extracting features from visual, audio, and text data, and experimenting with
different combinations, we developed a robust model that achieved an impressive
99% accuracy. Our methodology emphasizes the significance of feature
engineering in deception detection, providing a clear and interpretable
framework. We trained various machine learning models, including LSTM, BiLSTM,
and pre-trained CNNs, using both single and multi-modal approaches. The results
demonstrated that combining multiple modalities significantly enhances
detection performance compared to single modality training. This study
highlights the potential of strategic feature extraction and combination in
developing reliable and transparent automated deception detection systems in
video analysis, paving the way for more advanced and accurate detection
methodologies in future research.
