Extraction of Gender Specific Hidden Information From Head Related Transfer Function Using Machine Learning

Unique biological and behavioral characteristics are used for biometric identification because they contain reliable subject dependent information. Although conventional modalities such as facial geometry, vocal tract anatomy, fingerprints, iris patterns, and to some extent; gait also provide discriminative capabilities, their use is limited because of acquisition complexity, invasiveness, and sensitivity to environmental conditions. The growing need of noncontact, privacy-preserving biometric systems, research attention has shifted toward acoustic signals inherently being subject dependent. In this context, the Head Related Transfer Function (HRTF) has proven to be a reliable auditory biometric feature. It is direction and frequency dependent filtering of sound by the head, pinnae, torso, and shoulders. HRTF captures three cues: interaural time difference (ITD), interaural level difference (ILD), and pinna-induced spectral shaping. These spatially dependent cues vary in accordance with the morphological structure of the ear and its surrounding region. Our hypothesis is that HRTF not only encode subject specific information, gender-specific information may also be hidden within the