Biometric Authentication and Stationary Detection of Human Subjects by Deep Learning of Passive Infrared (PIR) Sensor Data

Abstract

Deep learning has shown to be capable of learning features from complex datasets such as in the case of biometric authentication. Biometric authentication relies on unique biological qualities to verify a user's identity against a group of potential adversaries for security purposes. In this paper, a deep learning dependent biometric authentication system based on chest motion data captured by a passive infrared (PIR) sensor, CM-PIR, is proposed. PIR sensors are cheap, commercial-off-the-shelf (COTS) components that are dependent on motion across its field of view (FoV) for accurate detection of human subjects. Thus, CM-PIR utilizes deep learning for the accurate detection of stationary human subjects, as well as for biometric authentication. CM-PIR collects chest motion data from sixteen human subjects in nine different home locations. Coefficients of Fourier transform (FFT), discrete wavelet transform (DWT), and the absolute value of an acceleration filter calculated from the raw voltage PIR values were selected as the optimal features for input to the deep learning models for biometric authentication. Preprocessing of these features included a threshold voltage range, normalization, and finally principal component analysis (PCA). CM-PIR is 94% accurate in stationary human presence detection and 75% accurate for biometric authentication using a RNN with a 90 second window size. This work provides a promising solution for stationary human detection and biometric authentication using a PIR sensor in a real-world setting.