Multimodal Face Recognition Using Motion Sensors and Voice Input

Abstract

The increasing demand for secure and reliable authentication systems has led to the development of multimodal biometric technologies that combine multiple sources of identity verification. Traditional unimodal systems, such as standalone face recognition, are often vulnerable to spoofing attacks, environmental variations, and high false acceptance or rejection rates. To address these limitations, this study proposes a Multimodal Face Recognition System Using Motion Sensors and Voice Input that integrates physiological and behavioral biometrics into a unified authentication framework. The proposed system combines three complementary modalities, facial recognition, motion-based behavioral analysis, and speaker verification. Facial features are extracted using deep convolutional neural networks (CNNs) trained on large-scale datasets to ensure high recognition accuracy under varying illumination and pose conditions. Motion data captured through embedded inertial sensors, including accelerometers and gyroscopes, is used to analyze dynamic head movement patterns during authentication, providing an additional behavioral biometric layer. Voice input is processed using Mel-Frequency Cepstral Coefficients (MFCCs) and classified using machine learning algorithms such as Support Vector Machines (SVM) or deep neural networks for speaker identification.

To enhance system performance, feature-level and decision-level fusion strategies are implemented to combine multimodal data effectively. Experimental evaluation demonstrates that the proposed system achieves higher accuracy, improved robustness against spoofing attacks, and lower False Acceptance Rate (FAR) and False Rejection Rate (FRR) compared to unimodal biometric systems. Furthermore, the integration of motion sensor data strengthens liveness detection, reducing vulnerability to photo, video, or replay-based attacks. The results indicate that multimodal biometric fusion significantly enhances security, reliability, and user trust. The proposed framework is particularly suitable for deployment in smartphones, IoT-enabled devices, banking systems, and high-security access control applications. This research contributes to the advancement of intelligent, sensor-assisted authentication systems for next-generation secure environments.