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.

scholarly journals CHOG Based EFD for Geometric Shape Retrieval of Images for Cloth and Object Invariant Gait Recognition

2019 ◽  
Vol 8 (4) ◽  
pp. 11887-11892
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Negative Impact ◽  
Gait Recognition ◽  
Svm Classifier ◽  
Person Identification ◽  
Hyper Plane ◽  
The Subject ◽  
Local Gradient ◽  
Region Splitting

Gait refers to person identification based on the observation of human walking style. One of the prominent hurdles in gait recognition is, the challenges posed by change in apparels like clothes and object held by the subject. The paper explores the feature extraction techniques like CHOG and Elliptical Fourier Descriptors in spatial and frequency domain respectively to mitigate this negative impact on gait recognition. The CHOG behavioural feature extraction technique is used to capture the effective distribution of local gradient on gait sequence images. Further the Elliptical Fourier Descriptor (EFD) is found in frequency domain to access the geometric characteristics of a spatial domain image. The work is carried out on 36 subjects with 5 different apparels and 3 different objects each with 6 gait cycles from standard dataset CASIA SET – B and CMU - MoBo. SVM classifier is used to effectively discriminate the gait cycle patterns using optimal hyper plane. The results obtained have given an improvement of 7% to 24% increase in gait recognition over earlier techniques like GEI, CDA, LDA, ENTROPY, static and dynamic region splitting.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
V. H. Nguyen ◽  
J. Mahowald ◽  
S. Maas ◽  
J.-C. Golinval
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Prestressed Concrete ◽  
Civil Engineering ◽  
Damage Identification ◽  
Real Life ◽  
Hankel Matrix ◽  
Principal Component ◽  
Engineering Structures ◽  
Civil Engineering Structures

The aim of this paper is to apply both time- and frequency-domain-based approaches on real-life civil engineering structures and to assess their capability for damage detection. The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. Damage detection is performed using the concept of subspace angles between a current (possibly damaged state) and a reference (undamaged) state. The first structure is the Champangshiehl Bridge located in Luxembourg. Several damage levels were intentionally created by cutting a growing number of prestressed tendons and vibration data were acquired by the University of Luxembourg for each damaged state. The second example consists in reinforced and prestressed concrete panels. Successive damages were introduced in the panels by loading heavy weights and by cutting steel wires. The illustrations show different consequences in damage identification by the considered techniques.

scholarly journals Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Md. Zasim Uddin ◽  
Daigo Muramatsu ◽  
Noriko Takemura ◽  
Md. Atiqur Rahman Ahad ◽  
Yasushi Yagi
Keyword(s):  
Gait Cycle ◽  
Gait Recognition ◽  
Real Life ◽  
Image Sequence ◽  
Body Parts ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Hinge Loss ◽  
Sequence Reconstruction ◽  
Spatio Temporal

AbstractGait-based features provide the potential for a subject to be recognized even from a low-resolution image sequence, and they can be captured at a distance without the subject’s cooperation. Person recognition using gait-based features (gait recognition) is a promising real-life application. However, several body parts of the subjects are often occluded because of beams, pillars, cars and trees, or another walking person. Therefore, gait-based features are not applicable to approaches that require an unoccluded gait image sequence. Occlusion handling is a challenging but important issue for gait recognition. In this paper, we propose silhouette sequence reconstruction from an occluded sequence (sVideo) based on a conditional deep generative adversarial network (GAN). From the reconstructed sequence, we estimate the gait cycle and extract the gait features from a one gait cycle image sequence. To regularize the training of the proposed generative network, we use adversarial loss based on triplet hinge loss incorporating Wasserstein GAN (WGAN-hinge). To the best of our knowledge, WGAN-hinge is the first adversarial loss that supervises the generator network during training by incorporating pairwise similarity ranking information. The proposed approach was evaluated on multiple challenging occlusion patterns. The experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art benchmarks.

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

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