Multi-Modal Face Authentication using Deep Visual and Acoustic Features

Abstract

User authentication on smartphones is the key to many applications, which must satisfy both security and convenience. We propose a multi-modal face authentication system, which pushes the limit of state-of-the-art image based face recognition solutions by incorporating a new dimension of sensing modality — acoustics. It actively emits almost inaudible acoustic signals from the earpiece speaker to “illuminate” the user's face and extracts features from the echoes using a customized convolutional neural network, which are fused with sophisticated visual features extracted from state-of-the-art face recognition models, for secure face authentication. Because the echo features depend on 3D facial geometries and material, our multi-modal design is not easily spoofed by images or videos like image based face recognition systems. It does not require any special sensors thus eliminating the extra costs in solutions like FaceID. Experiments show that our design achieves comparable face recognition performance to the state-of-the-art image based face authentication, while able to block image/video spoofing.