Multimodal biometric authentication method can conquer the defects of the unimodal biometric authentication technology. In this paper, we design and develop an efficient Android-based multimodal biometric authentication system with face and voice. Considering the hardware performance restriction of the smart terminal, including the random access memory (RAM), central processing unit (CPU) and graphics processor unit (GPU), etc., which cannot efficiently accomplish the tasks of storing and quickly processing the large amount of data, a face detection method is introduced to efficiently discard the redundant background of the image and reduce the unnecessary information. Furthermore, an improved local binary pattern (LBP) coding method is presented to improve the robustness of the extracted face feature. We also improve the conventional endpoint detection technology, i.e. the voice activity detection (VAD) method, which can efficiently increase the detection accuracy of the voice mute and transition information and boost the voice matching effectiveness. To boost the authentication accuracy and effectiveness, we present an adaptive fusion strategy which organically integrates the merits of the face and voice biometrics simultaneously. The cross-validation experiments with public databases demonstrate encouraging authentication performances compared with some state-of-the-art methods. Extensive testing experiments on Android-based smart terminal show that the developed multimodal biometric authentication system achieves perfect authentication effect and can efficiently content the practical requirements.

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