Improved gait recognition through gait energy image partitioning

2020 ◽  
Vol 36 (3) ◽  
pp. 1261-1274
Author(s):  
G. Premalatha ◽  
Premanand V Chandramani
Keyword(s):  
Gait Recognition ◽  
Gait Energy Image

Multi-View Gait Recognition Using Enhanced Gait Energy Image and Radon Transform Techniques

2015 ◽  
Vol 8 (2) ◽  
pp. 138-148 ◽  
Author(s):  
Iman Mohammed Burhan ◽  
Md. Jan Nordin
Keyword(s):  
Radon Transform ◽  
Gait Recognition ◽  
Gait Energy Image

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

2019 ◽  
Vol 78 (18) ◽  
pp. 26509-26536
Author(s):  
Chi Xu ◽  
Yasushi Makihara ◽  
Xiang Li ◽  
Yasushi Yagi ◽  
Jianfeng Lu
Keyword(s):  
Gait Recognition ◽  
Gait Energy Image

scholarly journals Comparative Study of Gait Gender Identification using Gait Energy Image (GEI) and Gait Information Image (GII)

2018 ◽  
Vol 197 ◽  
pp. 15006 ◽  
Author(s):  
Rosa Andrie Asmara ◽  
Irtafa Masruri ◽  
Cahya Rahmad ◽  
Indrazno Siradjuddin ◽  
Erfan Rohadi ◽  
...  
Keyword(s):  
Gait Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Gender Classification ◽  
Gender Identification ◽  
Walking Gait ◽  
Gait Energy Image ◽  
Surveillance Technology ◽  
Gender Based ◽  
Information Image

Identifying gender from the pedestrian video is one crucial key to study demographics in such areas. With current video surveillance technology, identifying gender from a distance is possible. This research proposed the utilization of computer vision to identify gender based on their walking gait. The data feature used to determine gender based on their walking gait divided into five parts, namely the head, chest, back, waist & buttocks, and legs. Two different methods are used to perform the real-time gender gait recognition process, i.e., Gait Energy Image (GEI) and Gait Information Image (GII), while the Support Vector Machine (SVM) method used as the data classifier. The experimental results show that the process of identifying gender based on walking with GEI method is 55% accuracy and GII method is 60% accuracy. From these results, it can conclude that the method GII with SVM classifier has the best accuracy in the process of gender classification

Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image

2019 ◽  
Author(s):  
Lingxiang Yao ◽  
Worapan Kusakunniran ◽  
Qiang Wu ◽  
Jian Zhang ◽  
Zhenmin Tang ◽  
...  
Keyword(s):  
Gait Recognition ◽  
Gait Energy Image

Gait Recognition Based on Modified Gait Energy Image

2018 ◽  
Author(s):  
Israel Raul Tinini Alvarez ◽  
Guillermo Sahonero-Alvarez
Keyword(s):  
Gait Recognition ◽  
Gait Energy Image

scholarly journals Improved Gait Recognition Accuracy Based on DFT- GEI

2021 ◽  
Author(s):  
Lavanya Sriniva
Keyword(s):  
Frequency Domain ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Principal Component ◽  
Physical Contact ◽  
Person Identification ◽  
Gait Energy Image ◽  
Rural Environments ◽  
Learning Techniques ◽  
Long Wave Infrared

Abstract Person identification is a challenging task in computer vision. Identify a person from different cameras due to changes in appearance based on cofactors. Cofactors such as changing clothes, a suitcase, backpack, etc. The gait biometric is used to identify a person vary with different cofactors at different backgrounds. The person's gait can be identified at a distance, based on a walking pattern, without any physical contact. In this work, the videos are recorded using Infrared and Visible cameras at different locations such as urban and rural environments. The pre-processing includes the recorded videos are converted into frames, person identification using deep learning techniques, background subtraction, artifacts removal, silhouettes extraction, calculating gait cycle, and synthesis frequency domain gait energy image by averaging the silhouettes. The moving features are extracted from the frequency domain gait energy image and gait energy image are dimensionally reduced by principal component analysis, recognized using different classifiers and results are compared. Experiments are conducted on urban and rural datasets recorded using Long Wave Infrared and Visible cameras.

Nonstandard Periodic Gait Energy Image for Gait Recognition and Data Augmentation

2019 ◽  
pp. 197-208
Author(s):  
Kejun Wang ◽  
Liangliang Liu ◽  
Yilong Lee ◽  
Xinnan Ding ◽  
Junyu Lin
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Gait Energy Image

Gait recognition based on Gabor wavelets and modified gait energy image for human identification

2013 ◽  
Vol 22 (4) ◽  
pp. 043039 ◽  
Author(s):  
Deng-Yuan Huang ◽  
Ta-Wei Lin ◽  
Wu-Chih Hu ◽  
Chih-Hsiang Cheng
Keyword(s):  
Gait Recognition ◽  
Human Identification ◽  
Gabor Wavelets ◽  
Gait Energy Image
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