Biometric Recognition Using Deep Learning: A Survey <s> Biometric Fusion <s> In this paper, we explore the encoding/pooling layer and loss function in the end-to-end speaker and language recognition system. First, a unified and interpretable end-to-end system for both speaker and language recognition is developed. It accepts variable-length input and produces an utterance level result. In the end-to-end system, the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation. Besides the basic temporal average pooling, we introduce a self-attentive pooling layer and a learnable dictionary encoding layer to get the utterance level representation. In terms of loss function for open-set speaker verification, to get more discriminative speaker embedding, center loss and angular softmax loss is introduced in the end-to-end system. Experimental results on Voxceleb and NIST LRE 07 datasets show that the performance of end-to-end learning system could be significantly improved by the proposed encoding layer and loss function. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Biometric Fusion <s> This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are averaged over all the frames of a single utterance to form an utterance-level feature. Our method utilizes an attention mechanism to give different weights to different frames and generates not only weighted means but also weighted standard deviations. In this way, it can capture long-term variations in speaker characteristics more effectively. An evaluation on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Biometric Fusion <s> Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets. However, enlarging dataset and models increases the computation and storage costs and cannot be done indefinitely. In this work, we seek to improve the identification and verification accuracy of a text-independent speaker recognition system without use of extra data or deeper and more complex models by augmenting the training and testing data, finding the optimal dimensionality of embedding space and use of more discriminative loss functions. Results of experiments on VoxCeleb dataset suggest that: (i) Simple repetition and random time-reversion of utterances can reduce prediction errors by up to 18%. (ii) Lower dimensional embeddings are more suitable for verification. (iii) Use of proposed logistic margin loss function leads to unified embeddings with state-of-the-art identification and competitive verification accuracies. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Biometric Fusion <s> The objective of this paper is speaker recognition under noisy and unconstrained conditions. ::: We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. ::: Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin. <s> BIB004
Single biometric recognition by itself is far from sufficient to solve all biometric/forensic tasks. For example distinguishing identical twins may not be possible from face only, or matching an identity from face with dis- Softmax VoxCeleb1 6.00 ResNet-34 BIB001 A-Softmax + GNLL VoxCeleb1 4.46 x-vector BIB002 Softmax VoxCeleb1 3.85 ResNet-20 BIB003 AM-Softmax VoxCeleb1 4.30 ResNet-50 BIB004 Softmax + Contrastive VoxCeleb2 3.95 Thin ResNet-34  Softmax VoxCeleb2 3.22 ResNet-28  AAM VoxCeleb1 0.95 Table 8 Reported EER of selected signature recognition models on GPDS dataset (using 10-12 genuine samples).