This paper describes an experiment using the Gaussian mixture models (GMM) classifier for biometric identification from photoplethysmography (PPG) signals acquired by wearable sensors. The PPG-based identification method represents a simpler replacement of electrocardiogram-based authentication systems having several practical limitations. The performance of the open-set GMM-based identification system was tested depending on the number of Gaussian mixtures and the length of processed PPG signals. The obtained overall mean subject identification accuracy about 88 % is promising in this state of research. The performed first-step experiments confirm that the proposed conception of a subject identification system using the second-derivate PPG wave taken in conditions of low magnetic field with radiofrequency disturbance is functional. However, prior to practical usage as a real-time application, some implementation and optimization tasks will have to be solved.