Deep Neural Spectral Subtraction Centroid Mel Frequency Powered Secure Multimodal Biometric Authentication

Objectives: To propose a Deep Neural Spectral Subtraction Centroid Mel Frequency Spatial-Temporal (DN-SCMFST) framework for robust multimodal biometric authentication (face and voice). Method: The system uses the Speaking Faces dataset (142 subjects, 13,000 samples). Preprocessing is performed with a Kushner–Stratonovich filter and spectral subtraction. Features are extracted via spectral centroid and spatio-temporal descriptors, followed by score-level fusion. Findings: DN-SCMFST achieved accuracy improvements of 5–18%, PSNR gains of 18–28%, FAR reduction of 20–37%, FRR reduction of 18–28%, and recognition time reduction of 15–27% compared with CNN+RNN and DL-MBA models. Novelty: Integrates deep neural spectral subtraction with centroid-based mel frequency and temporal-spatial fusion, offering resilience against noise and improved real-time recognition efficiency. Keywords: Multimodal biometric authentication, Deep neural network, Spectral subtraction, Spectral centroid, Mel frequency, Score-level fusion, Kushner–Stratonovich