Abstract: Disclosed herein are systems and methods for using machine learning for image-based spoof detection. One embodiment takes the form of a method that includes obtaining an input-data set that includes a plurality of images captured of a biometric-authentication subject by a plurality of cameras of a camera system. The method also includes inputting the input-data set into a trained machine-learning module, and processing the input-data set using the machine-learning module to obtain, from the machine-learning module, a spoof-detection result for the biometric-authentication subject. The method also includes outputting the spoof-detection result for the biometric-authentication subject.

Abstract: Disclosed herein are systems and methods for using machine learning for image-based spoof detection. One embodiment takes the form of a method that includes obtaining an input-data set that includes a plurality of images captured of a biometric-authentication subject by a plurality of cameras of a camera system. The method also includes inputting the input-data set into a trained machine-learning module, and processing the input-data set using the machine-learning module to obtain, from the machine-learning module, a spoof-detection result for the biometric-authentication subject. The method also includes outputting the spoof-detection result for the biometric-authentication subject.

Abstract: Embodiments relate to bioimpedence-based spoof detection in an optical biometric reader. For example, embodiments operate in context of a biometric reader having a platen integrated with substantially transparent bioimpedance source and receiver electrodes covered by a substantially transparent protective layer. A multi-frequency input signal can be injected from the source electrode into a purported skin site (through the protective layer), so that a response signal is received by the receiver electrode. The response signal is interrogated to formulate a dispersive bioimpedance response of the purported skin site to the input signal over multiple frequencies. A biometric spoof determination can then be made according to the dispersive bioimpedance response. The spoof determination can be used for spoof detection, presence detection, and/or other functions.

Abstract: Embodiments relate to bioimpedence-based spoof detection in an optical biometric reader. For example, embodiments operate in context of a biometric reader having a platen integrated with substantially transparent bioimpedance source and receiver electrodes covered by a substantially transparent protective layer. A multi-frequency input signal can be injected from the source electrode into a purported skin site (through the protective layer), so that a response signal is received by the receiver electrode. The response signal is interrogated to formulate a dispersive bioimpedance response of the purported skin site to the input signal over multiple frequencies. A biometric spoof determination can then be made according to the dispersive bioimpedance response. The spoof determination can be used for spoof detection, presence detection, and/or other functions.