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

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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Alzheimer's Disease Detection Using Extreme Learning Machine, Complex Dual Tree Wavelet Principal Coefficients and Linear Discriminant Analysis

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2018.2381 ◽

2018 ◽

Vol 8 (5) ◽

pp. 881-890 ◽

Cited By ~ 5

Author(s):

Debesh Jha ◽

Saruar Alam ◽

Jae-Young Pyun ◽

Kun Ho Lee ◽

Goo-Rak Kwon

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Extreme Learning Machine ◽

Disease Detection ◽

Linear Discriminant ◽

Learning Machine

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An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Sensors ◽

10.3390/s20061706 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1706 ◽

Cited By ~ 2

Author(s):

Dehua Zheng ◽

Zhen Hong ◽

Ning Wang ◽

Ping Chen

Keyword(s):

Discriminant Analysis ◽

Intrusion Detection ◽

Linear Discriminant Analysis ◽

Extreme Learning Machine ◽

Detection Efficiency ◽

Detection Algorithm ◽

Malicious Attacks ◽

Linear Discriminant ◽

Iot Devices ◽

Learning Machine

The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

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