Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification

Aref Farhadipour; Masoumeh Chapariniya; Teodora Vukovic; Volker Dellwo

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arXiv · 2409.00562

Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification

Aref Farhadipour·Masoumeh Chapariniya·Teodora Vukovic·Volker Dellwo

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Abstract

Multimodal learning involves integrating information from various modalities to enhance learning and comprehension. We compare three modality fusion strategies in person identification and verification by processing two modalities: voice and face. In this paper, a one-dimensional convolutional neural network is employed for x-vector extraction from voice, while the pre-trained VGGFace2 network and transfer learning are utilized for face modality. In addition, gammatonegram is used as speech representation in engagement with the Darknet19 pre-trained network. The proposed systems are evaluated using the K-fold cross-validation technique on the 118 speakers of the test set of the VoxCeleb2 dataset. The comparative evaluations are done for single-modality and three proposed multimodal strategies in equal situations. Results demonstrate that the feature fusion strategy of gammatonegram and facial features achieves the highest performance, with an accuracy of 98.37% in the person identification task. However, concatenating facial features with the x-vector reaches 0.62% for EER in verification tasks.

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Aref Farhadipour, Masoumeh Chapariniya, Teodora Vukovic, Volker Dellwo. 2024-08-31. Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification. https://arxiv.org/abs/2409.00562

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