The work proposes and implements BlowLive, a multi-factor biometric authentication framework that treats the acoustic signal of blowing onto a phone as a behavioral modality and a simultaneously captured face as a physiological modality, generating blow embeddings via GFCC features plus a CNN and facial embeddings via FaceNet, then deriving cryptographic keys from binarized fused embeddings through a fuzzy extractor, with an added Doppler-shift liveness detection module; on data collected from 50 participants it reports 99.56% accuracy for blow-acoustics and 100% for facial and fusion modalities, and 99.46% liveness detection accuracy under per-user thresholds.
The work proposes and implements BlowLive, a multi-factor biometric authentication framework that treats the acoustic signal of blowing onto a phone as a behavioral modality and a simultaneously captured face as a physiological modality, generating blow embeddings via GFCC features plus a CNN and facial embeddings via FaceNet, then deriving cryptographic keys from binarized fused embeddings through a fuzzy extractor, with an added Doppler-shift liveness detection module; on data collected from 50 participants it reports 99.56% accuracy for blow-acoustics and 100% for facial and fusion modalities, and 99.46% liveness detection accuracy under per-user thresholds.