Privacy-Preserving Facial Emotion Classification Dataset Using Visual Micro-Doppler Signatures from Ultra-Wideband (UWB) Radar

This dataset provides radar-based micro-Doppler measurements of facial expressions used for emotion classification in a privacy-preserving, contactless, and non-invasive manner. Data was captured using the XeThru X4M03 Ultra-Wideband (UWB) impulse radar sensor from 600 samples representing six basic emotions: anger, disgust, fear, happy, neutral, and sadness (100 samples each). Each sample corresponds to a 3-second radar signal stream, post-processed into time\u2013frequency spectrograms using Short-Time Fourier Transform (STFT). The resulting spectrograms were used to train deep learning (DL) classifiers, including VGG16, VGG19, ResNet50, and SqueezeNet. The ResNet50 model achieved the highest overall classification accuracy of 77% across all emotion classes. The dataset is intended to support research in non-intrusive emotion recognition, especially for integration into multi-modal hearing aids and cognitive-assistive technologies.