This paper presents an overview of polarimetric thermal imaging for biometrics, focusing on face recognition, with a short discussion on fingerprints and iris. Face recognition has been and continues to be an active area of biometrics research, with most of the research dedicated to recognition in the visible spectrum. However, face recognition in the visible spectrum is not practical for discrete surveillance in low-light and nighttime scenarios. Polarimetric thermal imaging represents an ideal modality for acquiring the naturally emitted thermal radiation from the human face, providing additional geometric and textural details not available in conventional thermal imagery. One of the main challenges lies in matching the acquired polarimetric thermal facial signature to gallery databases containing only visible facial signature, for interoperability with existing government biometric repositories. This paper discusses approaches and algorithms to exploit polarization information, as represented by the Stokes vectors, through feature extraction and nonlinear regression to enable polarimetric thermal-to-visible face recognition. In addition to cross-spectrum feature based approaches, crossspectrum image synthesis methods are discussed that seek to reconstruct a visible-like image given a polarimetric thermal face image input. Beyond facial biometrics, this paper presents an initial exploration of polarimetric thermal imaging for latent fingerprint acquisition. Latent prints are formed when the oils and sweat from the finger are deposited onto another surface through contact, and are typically collected by first dusting with powder before being imaged and then lifted with adhesive tape. This paper presents polarimetric thermal imagery of latent prints from a nonporous glass surface, acquired without the dusting process. A brief discussion of the utility of polarimetric thermal imaging for iris recognition is also presented.