Multimodal Biometric System Based on Autoencoders and Learning Vector Quantization

Abstract

This paper proposes a bimodal biometric verification system based on face and voice traits. The face characteristics are extracted using an autoencoder neural network. The voice characteristics are extracted using Mel-frequency cepstral coefficients. The matching procedure uses the Euclidean distance between one sample and the cluster centers obtained for each subject, through a learning vector quantization machine. The data fusion process is done through a simple normalization and sum of individual scores of the face-trait and the voice-trait. Several experiments are carried out varying the number of cluster centers, the size of the encoder output and the number of frames used for representing the voice trait of a subject. The performance of the biometric system is evaluated using the area under a receive operating characteristic (AUC of a ROC curve). The following performances are obtained: voice-trait biometric system: AUC = 0.877; face-trait biometric system: AUC = 0.94 and bimodal biometric system: AUC = 0.98. The database used, the MOBIO, was collected from 50 individuals (37 male and 13 female) using mobile phones.