Identification of Humans Using Hand Clapping Sounds

This paper demonstrates that hand clapping sounds could be employed as a useful biometric trait. The identity of 16 people was automatically recognized using their hand clapping sounds recorded with two mobile phones. To enhance the validity of the experiment, the audio recordings were made in six domestic environments (kitchen, living room, anteroom, and three bedrooms). The subjects were requested to clap their hands in three different hands configurations (A1, A3, and P1, using Repp’s taxonomy [1]). The three identification methods were compared. They were all based on the same classification algorithm (support vector machines) but differed in the way the acoustic features (cepstral coefficients) were extracted. In the first method, for each individual clap recording, the cepstral coefficients were derived only from the time frame exhibiting the highest energy. In the second method, the cepstral coefficients were computed for all the time frames and subsequently aggregated by calculating their mean values and standard deviations. In the third method, all the coefficients were preserved (no aggregation performed). The last-mentioned method produced the best results, yielding 99% and 61% identification accuracy for room-dependent and room-independent test conditions, respectively. Out of the three hands configuration compared, the one in which the hands were aligned straight to each other (P1) was the most conducive in terms of the identification accuracy.