mm-FERP: An effective method for human personality prediction via mm-wave radar using facial sensing - Imran, Naveed; Zhang, Jian; Yang, Zheng; Ali, Jehad (Information Processing and Management, 2025)
mm-FERP (millimeter wave Facial Expression Recognition for Personality) explores the use of mm-Wave radar technology, specifically the TI IWR1443, to assess personality traits based on the OCEAN model through facial expression analysis. This research uniquely combines psychological profiling with state-of-the-art technology to predict the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality traits by carefully analyzing facial muscle movements collected through mm-wave radar alongside detailed questionnaire analysis. Our advanced mm-FERP system employs mm-wave radar technology for the detection and analysis of facial expressions in a manner that is both non-intrusive and privacy-centric, handling the ethical and privacy concerns associated with traditional camera-based methods. Using a convolutional neural network (CNN), mm-FERP effectively analyzes the complex patterns in mm-wave signals. This approach enables the smooth transfer of model knowledge from extensive image-based (Scalograms) datasets to the detailed understanding of mm-wave radar signals, significantly enhancing the model's predictive accuracy and efficiency in identifying personality traits via emotional behavior. Our in-depth evaluation reveals mm-FERP's remarkable potential to predict personality traits through emotion recognition (Neutral, Smile, Angry, Sad, Amazed) with an impressive accuracy of 97% across distances up to 0.47 m. We experiment in a controlled environment with more than 50 participants from different age groups (18–35) including males and females of different continents to train our model on different facial symmetry. Each participant gives 50 samples 10 for each expression making a total of 2500 samples. We also collected a self-assessment report from the same participants of 64 questions related to psychological behavior to validate personality by correlating it with radar signal features on question value weight (0.5–1.5). mm-FERP achieve an average score of 97.8% in precision, 97.2% in Recall, and 97.2% of F1. These results show mm-FERP's ability as an innovative approach for psychological behavioral analysis through mm-wave emotion recognition, improving user experience design, and paving the path for interactive technologies that are both personalized and psychologically insightful.
Keywords: Deep learning; Emotion recognition; Facial expression recognition; Human facial expressions; Millimeter wave facial expression recognition for personality; Mm waves; Mm-wave radar; Openness, conscientiousness, extraversion, agreeableness, neuroticism personality prediction; Personality predictions; Wave radars; Deep learning; Emotion recognition; Human facial expression; mm-FERP; mm-wave Radar; OCEAN personality prediction