  Amongst all medical biometric traits, Photoplethysmograph (PPG) is the
easiest to acquire. PPG records the blood volume change with just combination
of Light Emitting Diode and Photodiode from any part of the body. With IoT and
smart homes' penetration, PPG recording can easily be integrated with other
vital wearable devices. PPG represents peculiarity of hemodynamics and
cardiovascular system for each individual. This paper presents non-fiducial
method for PPG based biometric authentication. Being a physiological signal,
PPG signal alters with physical/mental stress and time. For robustness, these
variations cannot be ignored. While, most of the previous works focused only on
single session, this paper demonstrates extensive performance evaluation of PPG
biometrics against single session data, different emotions, physical exercise
and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear
Discriminant Analysis (DLDA). When evaluated on different states and datasets,
equal error rate (EER) of $0.5\%$-$6\%$ was achieved for $45$-$60$s average
training time. Our CWT/DLDA based technique outperformed all other
dimensionality reduction techniques and previous work.
