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Scaled-Magnitude Multi-Channel Correlation Filters for Multimodal Biometric Recognition

We propose a novel variant of the multi-channel correlation filters (MCCF), namely the scaled-magnitude MCCF (SM-MCCF). The SM-MCCF is characterized by a scaling factor on the magnitude response, which has phase-only spectrum and conventional magnitude and phase spectra as the corner cases. We show that the SM-MCCF design technique, when applied to a multimodal biometric authentication system based on face and handwritten signature recognition, outperforms the conventional MCCF and SVM classifiers under low SNR conditions. Furthermore, the utility of the SM-MCCF is also explored for multimodal fusion with image features for face and handwritten signatures with i-vectors for speech data. Our experimental results indicate that SM-MCCF provides a reasonable improvement in performance, in terms of the EER and recognition rate, as opposed to the MCCF in both moderately and severely degraded scenarios. Moreover, we also demonstrate that the feature level fusion is advantageous than score fusion as the level of abstraction in feature representation is lesser when compared to score level representations.