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Evaluation of Bone-Conducted Cross-Talk Sound in the Head for Biometric Identification

Just like a fingerprint, every human head has its own uniqueness. To take advantage of its unique geometrical and physical characteristics, we can insert an inward-facing microphone into the ear canal to record sound coming from a bone conduction transducer on the mastoid on the contralateral side of the head. This sound is known as “cross-talk” sound, which is unique for each individual. In this study, we present an evaluation of bone-conducted “cross-talk” sound in the head for biometric user identification. Our approach relies on “cross-talk” sounds to estimate impulse responses (IRs) and uses them to identify the corresponding users. Mel frequency cepstral coefficients (MFCCs) are extracted from a 16-ms IR as acoustic features, and 1-nearest neighbor (1NN) classifier is used for making a decision. Finally, we evaluated the proposed approach with ten participants. Our results showed that “cross-talk” sounds could be used to identify users with an average accuracy of up to 99.8%, and the equal error rate (EER) obtained was 2.6% in user authentication. In addition, the IRs dataset and a video demonstrating how the system worked were made available on GitHub and YouTube.