Biometric Recognition Using Deep Learning: A Survey <s> Conclusions <s> Gait as a biometric feature that can be measured remotely without physical contact and proximal sensing has attract significant attention. This paper proposes to use con-volutional neural networks (ConvNets) and multi-task learning model(MLT) to identify human gait and to predict multiple human attributes simultaneously. In comparison to previous approaches, two novelty in our convolutional approach can be summarised as (i)using ConvNets to learn rich features from the training set is more generic and requires minimal domain knowledge of the problem compared to hand-craft feature, (ii) to identify human gait and to predict other human attributes simultaneously can achieve improved performance for all task than standalone gait identification. Specifically, we first extract Gait Energy Image(GEI) from each walking period as the low level input for the ConvNets. Secondly, we train the ConvNets through back-propagation using a joint loss of each task. Finally, high-level feature is hierarchically extracted in ConvNets, which is shared by each task and used to identify human gait and to predict attribute. The approach was verified on CASIA gait database B, achieving over 95.88% accuracy for each task. To the authors' best knowledge, this is the first time multi-attributes gait identification being proposed. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Conclusions <s> As the remarkable characteristics of remote accessed, robust and security, gait recognition has gained significant attention in the biometrics based human identification task. However, the existed methods mainly employ the handcrafted gait features, which cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification. Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. In particular, to further learn effective model with limited training data, we composite the gait energy images instead of raw sequence of gaits. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-arts. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Conclusions <s> This paper proposes a method of gait recognition using a convolutional neural network (CNN). Inspired by the great successes of CNNs in image recognition tasks, we feed in the most prevalent image-based gait representation, that is, the gait energy image (GEI), as an input to a CNN designed for gait recognition called GEINet. More specifically, GEINet is composed of two sequential triplets of convolution, pooling, and normalization layers, and two subsequent fully connected layers, which output a set of similarities to individual training subjects. We conducted experiments to demonstrate the effectiveness of the proposed method in terms of cross-view gait recognition in both cooperative and uncooperative settings using the OU-ISIR large population dataset. As a result, we confirmed that the proposed method significantly outperformed state-of-the-art approaches, in particular in verification scenarios. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Conclusions <s> This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Conclusions <s> Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D) without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.). Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP) dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way. <s> BIB005
In this work, we provided a summary of the recent deep learning-based models (till 2019) for biometric recognition. As opposed to the other surveys, it provides an overview of most used biometrics. Deep neural models have shown promising improvement over classical models for various biometrics. Some biometrics have BIB001 CASIA-B 95% Different scenes Yan et al BIB001 CASIA-B 30.55% Different views Alotaibi et al  CASIA-B 86.70% Different scenes Alotaibi et al  CASIA-B 85.51% Different views Wu et al BIB004 CASIA-B 84.67% Different views Li et al BIB005 OU-ISIR 95.04% -Zhang et al BIB002 OU-ISIR 95% -Wu et al BIB004 OU-ISIR 94.80% -Shiraga et al BIB003 OU-ISIR 90.45% attracted a lot more attention (such as face) due to the wider industrial applications, and availability of largescale datasets, but other biometrics seem to be following the same trend. Although deep learning research in biometrics has achieved promising results, there is still a great room for improvement in different directions, such as creating larger and more challenging datasets, addressing model interpretation, fusing multiple biometrics, and addressing security and privacy issues.