The pupil diameter (PD), controlled by the autonomic nervous system, seems to provide a strong indication of affective arousal, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line "relaxation" vs. "stress" differentiation are proposed. For the off-line approach, wavelet denoising, Kalman filtering, data normalization, and feature extraction are sequentially utilized. For the on-line approach, a hard threshold, a moving average window and three stress detection steps are implemented. In order to use only the most reliable data, two types of data selection methods (paired t test based on galvanic skin response (GSR) data and subject self-evaluation) are applied, achieving average classification accuracies up to 86.43 and 87.20% for off-line and 72.30 and 73.55% for on-line algorithms, with each set of selected data, respectively. The GSR was also monitored and processed in our experiments for comparison purposes, with the highest classification rate achieved being only 63.57% (based on the off-line processing algorithm). The overall results show that the PD signal is more effective and robust for differentiating "relaxation" vs. "stress," in comparison with the traditionally used GSR signal.