Small robots are being designed to recognize behaviors through playful interaction. Prior work used data from impoverished sensing devices such as inertial sensors to analyze gestures and attitude in playful interaction through time series analysis. However, the prior work did not focus on individual differences required for person identification. This research hypothesizes that person identification can be achieved by determining individual differences in playful interaction by using inertial sensor data. We propose a method that iteratively narrows down the candidates during interaction to achieve accurate person identification. This method calculates the features using a time series of the inertial sensor data. These features identify a candidate who is playfully interacting with the robot using a decision tree classifier that includes combinations of the current candidates. The system stores the results as a dataset for voting, and the voting results are used to reduce the candidates until the number of candidates is winnowed to one. Evaluation results show that our proposed method identifies persons through playful interactions with 99.1% accuracy.