On Input/Output Architectures for Convolutional Neural Network-Based Cross-View Gait Recognition

Takemura, Noriko; Makihara, Yasushi; Muramatsu, Daigo; Echigo, Tomio; Yagi, Yasushi

doi:10.1109/tcsvt.2017.2760835

Cited by 143 publications

(109 citation statements)

References 34 publications

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“…While the absolute values of EER are different because the number of subjects included in the datasets is different, the tendency is similar (i.e., the accuracy is better around α = 30 • and worse around α = 60 • ). Not only when using the same distance metric to our experiment (L2-norm between GEIs), the tendency is consistent when using state-of-the-art distance metric optimized by deep neural networks [30]. This result indicates the validity of our experiment based on simulation dataset.…”

Section: Comparison With Real Datasetsupporting

confidence: 78%

Directional characteristics evaluation of silhouette-based gait recognition

Shigeki

Okura

Mitsugami

et al. 2018

IPSJ T Comput Vis Appl

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Gait is an important biometric trait for identifying individuals. The use of inputs from multiple or moving cameras offers a promising extension of gait recognition methods. Personal authentication systems at building entrances, for example, can utilize multiple cameras installed at appropriate positions to increase their authentication accuracy. In such cases, it is important to identify effective camera positions to maximize gait recognition performance, but it is not yet clear how different viewpoints affect recognition performance. This study determines the relationship between viewpoint and gait recognition performance to construct standards for selecting an appropriate view for gait recognition using multiple or moving cameras. We evaluate the gait features generated from 3D pedestrian shapes to visualize the directional characteristics of recognition performance.

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Section: Comparison With Real Datasetsupporting

confidence: 78%

Directional characteristics evaluation of silhouette-based gait recognition

Shigeki

Okura

Mitsugami

et al. 2018

IPSJ T Comput Vis Appl

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“…We follow the evaluation method [21]. 10307 subjects are divided into two disjoint groups of approximately equal size, that is, 5153 training and 5154 testing subjects.…”

Section: B Experimental Results On Ou-mvlp Datasetmentioning

confidence: 99%

“…OU-ISIR Multi-View Large Population (OU-MVLP) [21] is the world's largest wide view variation gait database, which includes 10307 subjects (5114 males and 5193 females) from 14 view angles: 0°, 15°, 30°, 45°, 60°, 75°, 90°, 180°, 195°, 210°, 215°, 240°, 255°, 270°.…”

Section: A Datasetsmentioning

confidence: 99%

Gait recognition, generative adversarial networks,cross view

Zhang¹,

Yin²,

Zhou³

et al. 2019

2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019)

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The performance of gait recognition can be obviously affected by view angle variation. In this paper, we present a new method which uses a view transformation generative adversarial networks (GAN) to improve performance of dealing with cross-view gait recognition problem. Our proposed method firstly trains a convolutional neural network (CNN) using gait energy image (GEI) for recognition. Then, a GAN model is taken as a generator to transform gait images with variety view angle to unique side view images. In order to preserve the identification information of generated images, the generated images are input into the fixed pre-trained CNN and recognition loss is used to update generator. Finally, we combine the distance matrix of original and generated image and get final recognition results. We conduct experiments to demonstrate the improvement of adding GAN branch on three popular gait dataset. Experimental results show that our method can achieve state-of-the-art performance.

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“…While [44] and [43] proposed deep convolutional neural network (CNN) models using raw shilhouette images as the inputs, Shiraga et al [36] designed GEINet whose input is a single GEI. Some latest works [45,51] presented the CNN models with two inputs, where the similarities of these two inputs were learnt to discriminate between the same subject pairs and different subject pairs, and in [37], CNN architectures with different input and output were explored for gait verification and identification scenarios respectively. These approaches achieved superior performance in comparison to traditional methods, sufficiently enormous number of training samples, however, are required to obtain reliable CNN models, which are unsuitable to be applied for datasets with small sample size.…”

Section: Deep Learning-based Gait Recognitionmentioning

confidence: 99%

Speed-Invariant Gait Recognition Using Single-Support Gait Energy Image

Makihara

et al. 2019

Multimed Tools Appl

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Gait is one of the most popular behavioral biometrics because it can be authenticated at a distance from a camera without subject cooperation. Speed differences between matching pairs, however, cause significant performance drops in gait recognition, and gait mode difference (i.e., walking versus running) makes gait recognition further challenging. We therefore propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good trade-off between speed invariance and stability by aggregating multiple frames around single-support phases. In addition, to mitigate the pose differences between walking and running modes at single-support phases, we morph walking and running SSGEIs into intermediate SSGEIs between walking and running mode, where we exploit a free-form deformation field from the walking or running modes to the intermediate mode obtained by training data. We finally apply Gabor filtering and spatial metric learning as postprocessing for further accuracy improvement. Experiments on two publicly available datasets, the OU-ISIR Treadmill Dataset A and the CASIA-C Dataset demonstrate that the proposed method yields the state-of-the-art accuracies in both identification and verification scenarios with a low computational cost.

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On Input/Output Architectures for Convolutional Neural Network-Based Cross-View Gait Recognition

Cited by 143 publications

References 34 publications

Directional characteristics evaluation of silhouette-based gait recognition

Directional characteristics evaluation of silhouette-based gait recognition

Gait recognition, generative adversarial networks,cross view

Speed-Invariant Gait Recognition Using Single-Support Gait Energy Image

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