Gait Recognition-A Novel Approach to Quality Improvement in Human Silhouettes

2014 ◽  
Vol 573 ◽  
pp. 459-464 ◽  
Author(s):  
S.M.H. Sithi Shameem Fathima ◽  
R.S.D. Wahida Banu
Keyword(s):  
Quality Improvement ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Human Gait ◽  
Blob Detection ◽  
Novel Approach ◽  
Filter Output ◽  
Chinese Academy Of Sciences ◽  
Academy Of Sciences

This paper proposes a robust algorithm for the quality improvement in human silhouettes, to improve the gait recognition percentage of a person. In silhouette based gait recognition approach, the presence of incomplete and noisy silhouettes has a direct impact on recognition performance. Using blob detection, initially the incomplete silhouettes are identified. Fusion of frame difference energy image with dominant energy image of a silhouette along with a morphological filter output, preserve the kinetic and kinematic information to make incomplete silhouette into a high quality and a complete silhouette. The results prove that the resultant silhouettes are well suited for human gait recognition algorithm with improved variance. The silhouette database is taken from CASIA database. (Institute of Automation, Chinese Academy of Sciences).

scholarly journals A novel Approach to Human Gait Recognition using possible Speed Invariant features

2014 ◽  
Vol 7 (6) ◽  
pp. 1174-1193 ◽  
Author(s):  
Anup Nandy ◽  
Rupak Chakraborty ◽  
Pavan Chakraborty ◽  
G.C. Nandi
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Invariant Features ◽  
Novel Approach

scholarly journals Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jure Kovač ◽  
Peter Peer
Keyword(s):  
Walking Speed ◽  
Feature Fusion ◽  
Negative Impact ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Real Life ◽  
Human Gait ◽  
Dynamic Features ◽  
Skeleton Model ◽  
Model Based

Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

scholarly journals Cross-View Gait Recognition Based on Dual-Stream Network

2021 ◽  
Vol 25 (5) ◽  
pp. 671-678
Author(s):  
Xiaoyan Zhao ◽  
Wenjing Zhang ◽  
Tianyao Zhang ◽  
Zhaohui Zhang ◽  
◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Lower Limbs ◽  
Viewing Angle ◽  
Dynamic Features ◽  
Stream Network ◽  
Long Distance ◽  
Academy Of Sciences

Gait recognition is a biometric identification method that can be realized under long-distance and no-contact conditions. Its applications in criminal investigations and security inspections are thus broad. Most existing gait recognition methods adopted the gait energy image (GEI) for feature extraction. However, the GEI method ignores the dynamic information of gait, which causes the recognition performance to be greatly affected by viewing angle changes and the subject’s belongings and clothes. To solve these problems, in this paper a cross-view gait recognition method that uses a dual-stream network based on the fusion of dynamic and static features (FDSN) is proposed. First, the static features are extracted from the GEI and the dynamic features are extracted from the image sequence of the human’s lower limbs. Then, the two features are fused, and finally, a nearest neighbor classifier is used for classification. Comparative experiments on the CASIA-B dataset created by the Automation Institute of the Chinese Academy of Sciences showed that the FDSN achieves a higher recognition rate than a convolutional neural network (CNN) and Gaitset under changes in viewing angle or clothing. To meet our requirements, in this study a gait image dataset was collected and produced in a campus setting. The experimental results on this dataset show the effectiveness of the FDSN in terms of eliminating the effects of disruptive changes.

Gait Recognition Algorithm Based on the Trajectory of Tiptoe

2011 ◽  
Vol 255-260 ◽  
pp. 1984-1988
Author(s):  
Yi Bo Li ◽  
Qin Yang
Keyword(s):  
Wavelet Packet ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Small Sample ◽  
Knee Joints ◽  
Support Vector ◽  
Gait Characteristics ◽  
Camera Angle ◽  
Academy Of Sciences

Most researchers focus on the gait characteristics of hip and changed angle of knee joints, gait characteristics of foot is still less attention, also apply wavelet packet to analysis more detailed information of characteristics’ data, and use the support vector machine algorithm to reduce the randomness, it has their unique advantages in the small sample. Summarized the above three points of the paper, the paper proposes a new gait recognition method to extract trajectory of tiptoe, uses wavelet packet to analyze it, then applies SVM for classification and recognition. Tested at the NLPR database of Chinese Academy of Sciences of 45 camera angle, we observed that the recognition rate has significantly increased, we observed that the algorithm is an effective identification method.

scholarly journals Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Qiu ◽  
Huxian Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Performance ◽  
Human Subjects ◽  
Gait Recognition ◽  
Ensemble Empirical Mode Decomposition ◽  
Machine Learning Algorithms ◽  
Human Gait ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Phase Recognition

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.

scholarly journals Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hadi Sadoghi Yazdi ◽  
Hessam Jahani Fariman ◽  
Jaber Roohi
Keyword(s):  
High Precision ◽  
Gait Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Gait ◽  
Fusion Method ◽  
Data Set ◽  
Fuzzy Algorithm ◽  
Neuro Fuzzy ◽  
Gesture Classification

This paper presents a human gait recognition algorithm based on a leg gesture separation. Main innovation in this paper is gait recognition using leg gesture classification which is invariant to covariate conditions during walking sequence and just focuses on underbody motions and a neuro-fuzzy combiner classifier (NFCC) which derives a high precision recognition system. At the end, performance of the proposed algorithm has been validated by using the HumanID Gait Challenge data set (HGCD), the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time. And it has been compared to recent algorithm of gait recognition.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

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