Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution

Arshad, Habiba; Khan, Muhammad Attique; Sharif, Muhammad; Yasmin, Mussarat; Javed, Muhammad Awais

doi:10.1007/s13042-019-00947-0

Cited by 65 publications

(32 citation statements)

References 43 publications

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“…[24,25] is an improved algorithm based on canonical correlation analysis (CCA). e existing feature fusion algorithm [12][13][14][15]26] uses the neural network or sparse representation to jointly represent multimodal data, leading to suppress the relationship between multimodal data. CCA (20)(21)(22) can effectively model the relationship between multimodal data, but it cannot deal with the redundant information in the data.…”

Section: Low-rankmentioning

confidence: 99%

“…is method can effectively learn the potential features of multimodal data, ignoring the relationship between modes. In literature [15], a new approach for HGR is proposed which is based on Quartile Deviation of Normal Distribution (QDOND) for mortal extraction and Bayesian model along with binomial distribution for features fusion and best features selection.…”

Section: Introductionmentioning

confidence: 99%

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Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis

Dong

Zhou

2020

Computational and Mathematical Methods in Medicine

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As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer’s disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer’s disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method.

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Section: Low-rankmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis

Dong

Zhou

2020

Computational and Mathematical Methods in Medicine

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“…Although human action recognition (HAR) has found numerous applications, including intelligent video surveillance and retrieval, and robotics, during the last few decades, it is still considered a challenging problem by most researchers in the area of computer vision (CV) [1–5]. The existing HAR methods generally follow a cascaded design comprising two phases: the first includes frames preprocessing and segmentation, features extraction, and features reduction.…”

Section: Introductionmentioning

confidence: 99%

Improved strategy for human action recognition; experiencing a cascaded design

Khan¹,

Akram²,

Sharif³

et al. 2020

IET Image Processing

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“…A person's gait, or way of walking, is a complex spatiotemporal biological feature that can be used to distinguish an individual [3], and hence, to realize personal identification [4]. Research [5] shows that the human gait is unique, and it is difficult to fake.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Based on the Feature Extraction of Gabor Filter and Linear Discriminant Analysis and Improved Local Coupled Extreme Learning Machine

Guo

Zhang

et al. 2020

Mathematical Problems in Engineering

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A gait energy image contains much gait information, which is one of the most effective means to recognize gait characteristics. The accuracy of gait recognition is greatly affected by covariates, such as the viewing angle, occlusion of clothing, and walking speed. Gait features differ somewhat by angles. Therefore, how to improve the recognition accuracy of a cross-view gait is a challenging task. This study proposes a new gait recognition algorithm structure. A Gabor filter is used to extract gait features from gait energy images, since it can extract features of different directions and scales. We use linear discriminant analysis (LDA) to tackle the problem that the feature dimension restricts the process. Finally, the improved local coupled extreme learning machine based on particle swarm optimization is used for the classification process of the extracted features of the gait. The proposed method and other current mainstream algorithms are compared in terms of the recognition accuracy based on the CASIA-A and CASIA-B datasets, and the simulation results show that the proposed algorithm has good performance and performs well at cross-view gait recognition.

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Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution

Cited by 65 publications

References 43 publications

Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis

Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis

Improved strategy for human action recognition; experiencing a cascaded design

Gait Recognition Based on the Feature Extraction of Gabor Filter and Linear Discriminant Analysis and Improved Local Coupled Extreme Learning Machine

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