Biometric Recognition Using Deep Learning: A Survey <s> Performance of Models for Voice Recognition: <s> A new deep multitask learning based metric learning method is proposed.An appropriate architecture is proposed for offline signature verification.The knowledge of other signers' samples is transfered for better accuracy.The proposed method outperforms SVM and Discriminative Deep Metric Learning.It outperforms the accuracy of other published reports. Display Omitted This paper presents a novel classification method, Deep Multitask Metric Learning (DMML), for offline signature verification. Unlike existing methods that to verify questioned signatures of an individual merely consider the training samples of that class, DMML uses the knowledge from the similarities and dissimilarities between the genuine and forged samples of other classes too. To this end, using the idea of multitask and transfer learning, DMML train a distance metric for each class together with other classes simultaneously. DMML has a structure with a shared layer acting as a writer-independent approach, that is followed by separated layers which learn writer-dependent factors. We compare the proposed method against SVM, writer-dependent and writer-independent Discriminative Deep Metric Learning method on four offline signature datasets (UTSig, MCYT-75, GPDSsynthetic, and GPDS960GraySignatures) using Histogram of Oriented Gradients (HOG) and Discrete Radon Transform (DRT) features. Results of our experiments show that DMML achieves better performance compared to other methods in verifying genuine signatures, skilled and random forgeries. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Performance of Models for Voice Recognition: <s> Contactless 2D fingerprint identification is more hygienic, and enables deformation free imaging for higher accuracy. Success of such emerging contactless fingerprint technologies requires advanced capabilities to accurately match such fingerprint images with the conventional fingerprint databases which have been developed and deployed in last two decades. Convolutional neural networks have shown remarkable success for the face recognition problem. However, there has been very few attempts to develop CNN-based methods to address challenges in fingerprint identification problems. This paper proposes a multi-Siamese CNN architecture for accurately matching contactless and contact-based fingerprint images. In addition to the fingerprint images, hand-crafted fingerprint features, e.g. minutiae and core point, are also incorporated into the proposed architecture. This multi-Siamese CNN is trained using the fingerprint images and extracted features. Therefore, a more robust deep fingerprint representation is formed from the concatenation of deep feature vectors generated from multi-networks. In order to demonstrate the effectiveness of the proposed approach, a publicly available database consisting of contact-based and respective contactless finger-prints is utilized. The experimental evaluations presented in this paper achieve outperforming results, over other CNN-based methods and the traditional fingerprint cross matching methods, and validate our approach. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Performance of Models for Voice Recognition: <s> The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network — MENet, for Minutiae Extraction Network — to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Performance of Models for Voice Recognition: <s> Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is proposing a new deep learning architecture for fingerprint recognition. The proposed architecture comprises of a pre-processing stage for extracting texture features from fingerprints, and this stage is performed by using histogram equalization, Gabor enhancement and fingerprint thinning. The pre-processed fingerprints are input into a Deep Convolutional Neural Network classifier. The proposed approach has achieved 98.21% classification accuracy with 0.9 loss. The obtained accuracy is significantly higher than previously reported results on the same dataset, 77%. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Performance of Models for Voice Recognition: <s> Fingerprint recognition has been utilized for cellphone authentication, airport security and beyond. Many different features and algorithms have been proposed to improve fingerprint recognition. In this paper, we propose an end-to-end deep learning framework for fingerprint recognition using convolutional neural networks (CNNs) which can jointly learn the feature representation and perform recognition. We train our model on a large-scale fingerprint recognition dataset, and improve over previous approaches in terms of accuracy. Our proposed model is able to achieve a very high recognition accuracy on a well-known fingerprint dataset. We believe this framework can be widely used for biometrics recognition tasks, making more scalable and accurate systems possible. We have also used a visualization technique to highlight the important areas in an input fingerprint image, that mostly impact the recognition results. <s> BIB005
The most widely used metric for evaluation of speaker recognition systems is Equal Error Rate (EER). Apart from EER, other metrics are also used for system evaluation. For example, detection error trade-off curve (DET curve) is used in SRE performance evaluations to compare different systems. A DET curve is created by plotting the false negative rate versus false positive rate, with logarithmic scale on the x-and y-axes. (EER corresponds to the point on a DET curve where false negative rate and false positive rate are equal.) Minimum detection cost is another metric that is frequently used in speaker recognition tasks  . This cost is defined as a weighted average of two normalized error rates. Not all of these metrics are reported in every research papers, but EER is the most important metric to compare different systems. Table 7 records the performance of some of the best deep leaning based spearker recognition systems on Vox-Celeb1 dataset. As is shown in the table, the progress made by researchers over the last two years are prominent. All these systems shown in Table 7 are single sys- Table 3 Accuracy of several fingerprint recognition algorithms. Method Dataset Performance FingerNet BIB005 PolyU Acc=95.70% Multi-Siamese BIB002 PolyU EER=8.39% MENet BIB003 FVC 2002 EER=0.78% MENet BIB003 FVC 2004 EER=5.45% Deep CNN BIB004 Composed Acc=98.21% tems, which means the performance can be boosted further with system combination or ensembles. For SRE datasets, due to the large number of its series and complexity of different evaluation conditions, it is hard to compile all results into one table. Also different papers may present results on different sets or conditions, making it hard to compare the performance across different approaches. The deep learning-based approaches discussed above have also been applied to other related areas, e.g. speech diarization, replay attack detection and language identification. Since this paper focuses on biometric recognition, we skip the details for these tasks. Performance of Models for Signature Recognition: Most signature recognition works use EER as the performance metric, but sometimes, they also report accuracy. Table 8 summarizes the EER of several signature verification methods on GPDS dataset, where there are 12 authentic signature samples used for each person (except in BIB001 where it is 10 samples). In addition, Table 9 provides the reported accuracy of a few other works on other datasets.