Face recognition algorithms typically deal with the classification of static images of faces that are obtained using a camera. In this paper we propose a new sensing mechanism based on the Doppler effect to capture the patterns of motion of talking faces. We incident an ultrasonic tone on subjects ’ faces and capture the reflected signal. When the subject talks, different parts of their face move with different velocities in a characteristic manner. Each of these velocities imparts a different Doppler shift to the reflected ultrasonic signal. Thus, the set of frequencies in the reflected ultrasonic signal is characteristic of the subject. We show that even using a simple feature computation scheme to characterize the spectrum of the reflected signal, and a simple GMM based Bayesian classifier, we are able to recognize talkers with an accuracy of over 90%. Interestingly, we are also able to identify the gender of the talker with an accuracy of over 90%
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