Cross-view gait recognition using view-dependent discriminative analysis

Mansur, Al; Makihara, Yasushi; Muramatsu, Daigo; Yagi, Yasushi

doi:10.1109/btas.2014.6996272

Cited by 33 publications

(30 citation statements)

References 32 publications

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“…This family usually applies machine learning-based approaches such as linear discriminant analysis (LDA) [8,9], primal rank support vector machine (RankSVM) [10], and multi-view discriminant analysis (MvDA) [11]. Moreover, discriminative approaches using deep learning [12] has become flourishing recently.…”

Section: Introductionmentioning

confidence: 99%

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

Takemura

Makihara

Muramatsu

et al. 2018

IPSJ T Comput Vis Appl

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309

204

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This paper describes the world's largest gait database with wide view variation, the "OU-ISIR gait database, multi-view large population dataset (OU-MVLP)", and its application to a statistically reliable performance evaluation of vision-based cross-view gait recognition. Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0°−90°, 180°−270°. In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods.

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Section: Introductionmentioning

confidence: 99%

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

Takemura

Makihara

Muramatsu

et al. 2018

IPSJ T Comput Vis Appl

Self Cite

309

204

View full text Add to dashboard Cite

show abstract

“…In this work, we follow protocol used in [21], only a subset of OULP-C1V1 is used to test our method. The subset contains 1912 subjects.…”

Section: Datasetmentioning

confidence: 99%

“…93.6 81.2 62.2 83.1 97.1 96.3 89.3 74.5 89.3 98.2 98.2 95.5 87.8 94.9 65 90.9 95.3 95.5 90.2 93.0 94.8 97.7 97.7 94.6 96.2 97.6 98.4 98.8 97.6 98.1 75 77.5 94.4 96.0 94.2 90.5 87.2 96.9 98.3 97.4 95.0 93.6 98.7 99.0 98.5 97.5 85 55.4 87.1 94.8 94.7 83.0 68.9 93.7 97.0 97.9 89.4 83.3 96.8 98.5 98.6 94.3 Performance of our method on OULP-C1V1 following protocol used in[21] in terms of Rank-1, Rank-2 and Rank-5 recognition rates.…”

mentioning

confidence: 99%

Feature map pooling for cross-view gait recognition based on silhouette sequence images

Chen

Wang

Liu

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner. Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.

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“…GEI describes how motion proceeds and which regions are more involved in motion, the brighter it is in the GEI image. Several gait recognition approaches relied on features extracted from GEIs (Li et al, 2012;Huang et al, 2013;Wang et al, 2014;Mansur et al, 2014). However, they used reduced-dimensionality GEIs or applied the feature extraction algorithm on the holistic GEI.…”

Section: Related Workmentioning

confidence: 99%

Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns

Binsaadoon

El-Alfy

2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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With the increasing security breaches nowadays, automated gait recognition has recently received increasing importance in video surveillance technology. In this paper, we propose a method for human identification at distance based on Fuzzy Local Binary Pattern (FLBP). After the Gait Energy Image (GEI) is generated as a spatiotemporal summary of a gait video sequence, a multi-region partitioning is applied and FLBP based features are extracted for each region. We also evaluate the performance under the variation of some factors including viewing angle, clothing and carrying conditions. The experimental work showed that GEI-FLBP with partitioning has remarkably enhanced the identification accuracy.

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Cross-view gait recognition using view-dependent discriminative analysis

Cited by 33 publications

References 32 publications

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

Feature map pooling for cross-view gait recognition based on silhouette sequence images

Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns

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