Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Gait Recognition <s> In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Gait Recognition <s> In this work we present a deep convolutional neural network using 3D convolutions for Gait Recognition in multiple views capturing spatio-temporal features. A special input format, consisting of the gray-scale image and optical flow enhance color invaranice. The approach is evaluated on three different datasets, including variances in clothing, walking speeds and the view angle. In contrast to most state-of-the-art Gait Recognition systems the used neural network is able to generalize gait features across multiple large view angle changes. The results show a comparable to better performance in comparison with previous approaches, especially for large view differences. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Gait Recognition <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> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Gait Recognition <s> Comparing with other biometrics, gait has advantages of being unobtrusive and difficult to conceal. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by average person, which makes it very convenient and inexpensive to collect gait data. In this paper, we study gait recognition using smartphones in the wild. Unlike traditional methods that often require the person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under a condition of unconstraint without knowing when, where, and how the user walks. To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space domain and in the time domain are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones on a total of 118 subjects are used for evaluations. Experiments show that the proposed method achieves over 93.5% and 93.7% accuracy in person identification and authentication, respectively. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Gait Recognition <s> Abstract We face the problem of gait recognition by using a robust deep learning model based on graphs. The proposed graph based learning approach, named Time based Graph Long Short-Term Memory (TGLSTM) network, is able to dynamically learn graphs when they may change during time, like in gait and action recognition. Indeed, the TGLSTM model jointly exploits structured data and temporal information through a deep neural network model able to learn long short-term dependencies together with graph structure. The experiments were made on popular datasets for action and gait recognition, MSR Action 3D, CAD-60, CASIA Gait B, “TUM Gait from Audio, Image and Depth” (TUM-GAID) datasets, investigating the advantages of TGLSTM with respect to state-of-the-art methods . <s> BIB005
Research on gait recognition based on deep learning has only taken off in the past few years. In one of the older works, Wolf et al BIB002 proposed a gait recognition system using 3D convolutional neural networks which learns the gait from multiple viewing angles. This model consists of multiple layers of 3D convolutions, max pooling and ReLUs, followed by fully-connected layers. Zhang et al BIB003 proposed a Siamese neural network for gait recognition, in which the sequences of images are converted into gait energy images (GEI) BIB001 . Next, they are fed to the twin CNN networks and their contrastive losses are also calculated. This allows the system to minimize the loss for similar inputs and maximize it for different ones. The network for this work is shown in Figure 19 . Battistone et al BIB005 proposed gait recognition through a time-based graph LSTM net- Fig. 19 Siamese network for gait recognition, courtesy of BIB003 . work, which uses alternating recursive LSTM layers and dense layers to extract skeletons from the person's images and learn their joint features. Zou et al BIB004 proposed a hybrid CNN-RNN network which uses the data from smartphone sensors for gait recognition, particularly from the accelerometer and the gyroscope, and the subjects are not restricted in their walking in any way.