The field of biometric research continues to grow, with new modalities being explored for both the physical and behavioral domains. Along with these new modalities, researchers are exploring using different types of sensors to acquire the biometric raw signal. Hyperspectral imaging (HSI) represents an untapped sensor for biometric research. HSI provides a wealth of new imaging data based on specific wavelengths outside the traditional imaging sensors operating in the human visual perception range. Hence, exploring advanced imaging instrumentation represents a viable alternative to traditional systems. Traditional imaging systems may restrain the information that can be explored in a biometric trait. By combining imaging with spectroscopy, HSI generates data in hundreds of wavelengths and can attain microscopic properties, enabling an in-depth, sophisticated image-based representation. Therefore, using HSI for physical biometric traits can provide richer spectral information that can be leveraged to enhance biometric identity matching. These enhancements may increase accuracy, secure the biometric trait under examination, increase fairness as the sensor examines elements of the trait that are not correlated to known biases, e.g., skin tone for face recognition, and increase usability as the sensor can detect additional features like biological sex-related features. This manuscript reviews valuable analysis of face recognition, human tissue, and latent fingerprint detection using hyperspectral imaging. Further, it explores a transformative approach to acquiring and processing data for fingerprint systems by integrating spatial information with biochemical content that could only be detected by imaging sensors over a wide spectral range. Finally, we identify interesting and promising open research directions to engage scientists to contribute to those areas.