123 research outputs found
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN)
architecture has been proposed for speaker verification in the text-independent
setting. One of the main challenges is the creation of the speaker models. Most
of the previously-reported approaches create speaker models based on averaging
the extracted features from utterances of the speaker, which is known as the
d-vector system. In our paper, we propose an adaptive feature learning by
utilizing the 3D-CNNs for direct speaker model creation in which, for both
development and enrollment phases, an identical number of spoken utterances per
speaker is fed to the network for representing the speakers' utterances and
creation of the speaker model. This leads to simultaneously capturing the
speaker-related information and building a more robust system to cope with
within-speaker variation. We demonstrate that the proposed method significantly
outperforms the traditional d-vector verification system. Moreover, the
proposed system can also be an alternative to the traditional d-vector system
which is a one-shot speaker modeling system by utilizing 3D-CNNs.Comment: Accepted to be published in IEEE International Conference on
Multimedia and Expo (ICME) 201
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.Comment: 5 pages, 4 figures, Accepted in 40th IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 15
Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation
We introduce caption-guided face recognition (CGFR) as a new framework to
improve the performance of commercial-off-the-shelf (COTS) face recognition
(FR) systems. In contrast to combining soft biometrics (eg., facial marks,
gender, and age) with face images, in this work, we use facial descriptions
provided by face examiners as a piece of auxiliary information. However, due to
the heterogeneity of the modalities, improving the performance by directly
fusing the textual and facial features is very challenging, as both lie in
different embedding spaces. In this paper, we propose a contextual feature
aggregation module (CFAM) that addresses this issue by effectively exploiting
the fine-grained word-region interaction and global image-caption association.
Specifically, CFAM adopts a self-attention and a cross-attention scheme for
improving the intra-modality and inter-modality relationship between the image
and textual features, respectively. Additionally, we design a textual feature
refinement module (TFRM) that refines the textual features of the pre-trained
BERT encoder by updating the contextual embeddings. This module enhances the
discriminative power of textual features with a cross-modal projection loss and
realigns the word and caption embeddings with visual features by incorporating
a visual-semantic alignment loss. We implemented the proposed CGFR framework on
two face recognition models (ArcFace and AdaFace) and evaluated its performance
on the Multi-Modal CelebA-HQ dataset. Our framework significantly improves the
performance of ArcFace in both 1:1 verification and 1:N identification
protocol.Comment: This article has been accepted for publication in the IEEE
International Joint Conference on Biometrics (IJCB), 202
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