DETECTING DECEPTION USING MACHINE LEARNING WITH FACIAL EXPRESSIONS AND PULSE RATE
Abstract:
Deception detection is a challenging task with significant implications in various domains, including law enforcement, security, and psychology. Traditional methods of detecting deception often rely on subjective human judgment, leading to inconsistent and unreliable results. However, recent advancements in machine learning and the availability of physiological data have presented new opportunities to develop more accurate and objective deception detection systems.
This research proposes a novel approach to detecting deception by leveraging machine learning techniques with facial expressions and pulse rate. Facial expressions are widely recognized as important non-verbal cues that can convey emotions and intentions. Similarly, pulse rate, as a physiological measure, reflects changes in emotional and cognitive states and can be indicative of deceptive behavior.
The proposed system consists of two main stages: feature extraction and classification. In the feature extraction stage, facial expression analysis algorithms are utilized to extract relevant features from facial images, capturing subtle changes in facial muscle movements associated with deception. Additionally, pulse rate data is collected using non-invasive wearable devices, such as smartwatches or fitness trackers.
In the classification stage, machine learning models, such as support vector machines (SVM), random forests, or deep neural networks, are trained using the extracted features and pulse rate data. These models learn patterns and relationships from a labeled dataset of deceptive and non-deceptive samples to predict the likelihood of deception in real-time.
To evaluate the proposed approach, a comprehensive dataset comprising both deceptive and non-deceptive individuals is collected, including controlled scenarios and real-life situations. Various performance metrics, such as accuracy, precision, recall, and F1 score, are employed to assess the effectiveness of the deception detection system.
The results demonstrate that the integration of facial expressions and pulse rate data significantly improves the accuracy and reliability of deception detection compared to using either modality alone. The proposed system shows promise for applications in security screenings, interrogations, and other contexts where deception detection is crucial.
Keywords: Deception detection, machine learning, facial expressions, pulse rate, non-verbal cues, physiological signals.