Hearing versus Seeing Identical Twins

Identical twins pose a great challenge to face recognition systems due to their similar appearance. Nevertheless, even though twins may look alike, we believe they speak differently. Hence we propose to use their voice patterns to distinguish between twins. Voice is a natural signal to produce, and it is a combination of physiological and behavioral biometrics, therefore it is suitable for twin verification. In this paper, we collect an audio-visual database from 39 pairs of identical twins. Three types of typical voice features are investigated, including Pitch, Linear Prediction Coefficients (LPC) and Mel Frequency Cepstral Coefficients (MFCC). For each type of voice feature, we use Gaussian Mixture Model to model the voice spectral distribution of each subject, and then employ the likelihood ratio of the probe belonging to different classes for verification. The experimental results on this database demonstrate a significant improvement by using voice over facial appearance to distinguish between identical twins. Furthermore, we show that by fusion both types of biometrics, recognition accuracy can be improved.