With　the　rapid　development　of　mobile　devices　and　the　fast　increase　of　sensitive　data,　secure　and　convenient　mobile　authentication　technologies　are　desired.　Except　for　traditional　passwords,　many　mobile　devices　have　biometric-based　authentication　methods　(e.g.,　fingerprint,　voiceprint,　and　face　recognition),　but　they　are　vulnerable　to　spoofing　attacks.　To　solve　this　problem,　we　study　new　biometric　features　which　are　based　on　the　dental　occlusion　and　find　that　the　bone-conducted　sound　of　dental　occlusion　collected　in　binaural　canals　contains　unique　features　of　individual　bones　and　teeth.　Motivated　by　this,　we　propose　a　novel　authentication　system,　TeethPass,　which　uses　earbuds　to　collect　occlusal　sounds　in　binaural　canals　to　achieve　authentication.　We　design　an　event　detection　method　based　on　spectrum　variance　and　double　thresholds　to　detect　bone-conducted　sounds.　Then,　we　analyze　the　time-frequency　domain　of　the　sounds　to　filter　out　motion　noises　and　extract　unique　features　of　users　from　three　aspects:　bone　structure,　occlusal　location,　and　occlusal　sound.　Finally,　we　design　an　incremental　learning-based　Siamese　network　to　construct　the　classifier.　Through　extensive　experiments　including　22　participants,　the　performance　of　TeethPass　in　different　environments　is　verified.　TeethPass　achieves　an　accuracy　of　96.8%　and　resists　nearly　99%　of　spoofing　attacks.