Emotion Recognition from Human Gait Features Based on DCT Transform

2019 ◽  
pp. 511-517
Author(s):  
Penghui Xue ◽  
Baobin Li ◽  
Ning Wang ◽  
Tingshao Zhu
Keyword(s):  
Emotion Recognition ◽  
Human Gait ◽  
Dct Transform ◽  
Gait Features

Automatic Emotion Recognition Based on Non-Contact Gaits Information

2019 ◽  
pp. 54-67
Author(s):  
Jingying Wang ◽  
Baobin Li ◽  
Changye Zhu ◽  
Shun Li ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Emotional State ◽  
Experimental Results ◽  
Learning Models ◽  
Face Expression ◽  
Gait Features ◽  
Emotion Labels ◽  
Machine Learning Models

Automatic emotion recognition was of great value in many applications; however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Except face expression and voices, human gaits could reflect the walker's emotional state too. By utilizing 59 participants' gaits data with emotion labels, the authors train machine learning models that are able to “sense” individual emotion. Experimental results show these models work very well and prove that gait features are effective in characterizing and recognizing emotions.

scholarly journals Characterization of walking gait features using Kinect

2021 ◽  
Author(s):  
Peter Müller ◽  
Ádam Schiffer
Keyword(s):  
Gait Cycle ◽  
Movement Analysis ◽  
Body Motion ◽  
Human Gait ◽  
Physiotherapy Treatment ◽  
Walking Gait ◽  
Gait Features ◽  
Analysis System ◽  
Reduced Mobility

AbstractThe examination of the human gait cycle can be useful for physiotherapists for identifying and/or predicting body motion disorders and it provides important data about the patient's condition in many ways. In this paper, the progress of a special TheraSuit physiotherapy treatment of a child, who has reduced mobility due to cerebral palsy, has been investigated. Generally, this type of disorder is classified into strict levels and the effectiveness of the therapy is expressed by changing between distinct levels. On the other hand paper describes a new markerless self-developed movement analysis system, which is able to show the effectiveness of the treatment with quantitative parameters. These parameters are determined by statistical methods.

scholarly journals Human gait-based gender classification using neutral and non-neutral gait sequences

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Zhyar Q. Mawlood ◽  
Azhin T. Sabir
Keyword(s):  
Wavelet Transform ◽  
Experimental Result ◽  
Human Gait ◽  
Biometric Identification ◽  
Automatic Identification ◽  
Biometric System ◽  
Biometric Recognition ◽  
Gait Features ◽  
The Individual ◽  
Gait Database

A biometric system offers automatic identification of an individual basedon characteristic possessed by the individual. Biometric identification systems are often categorized as physiological or behavioural characteristics.Gait as one of the behavioural biometric recognition aims to recognizean individual by the way he/she walk. In this paper we propose genderclassification based on human gait features using wavelet transform andinvestigates the problem of non-neutral gait sequences; Coat Wearing andcarrying bag condition as addition to the neutral gait sequences. We shallinvestigate a new set of feature that generated based on the Gait Energy Image and Gait Entropy Image called Gait Entropy Energy Image(GEnEI). Three different feature sets constructed from GEnEI basedon wavelet transform called, Approximation coefficient Gait EntropyEnergy Image, Vertical coefficient Gait Entropy Energy Image and Approximation & Vertical coefficients Gait Entropy Energy Image Finallytwo different classification methods are used to test the performance ofthe proposed method separately, called k-nearest-neighbour and SupportVector Machine. Our tests are based on a large number of experimentsusing a well-known gait database called CASIA B gait database, includes124 subjects (93 males and 31 females). The experimental result indicatesthat the proposed method provides significant results and outperform thestate of the art.

Gait Based Biometric Authentication System with Reduced Search Space

2016 ◽  
pp. 296-320
Author(s):  
L. R. Sudha ◽  
R. Bhavani
Keyword(s):  
Rejection Rate ◽  
Search Space ◽  
Human Gait ◽  
Confined Space ◽  
Security Enhancement ◽  
Novel Approach ◽  
Gait Features ◽  
Sequential Feature Selection ◽  
Combined Classifier ◽  
Modern Era

Deployment of human gait in developing new tools for security enhancement has received growing attention in modern era. Since the efficiency of any algorithm depends on the size of search space, the aim is to propose a novel approach to reduce the search space. In order to achieve this, the database is split into two based on gender and the search is restricted in the identified gender database. Then highly discriminant gait features are selected by forward sequential feature selection algorithm in the confined space. Experimental results evaluated on the benchmark CASIA B gait dataset with the newly proposed combined classifier kNN-SVM, shows less False Acceptance Rate (FAR) and less False Rejection Rate (FRR).

scholarly journals Recognition of emotions using Kinects

2015 ◽  
Author(s):  
Shun Li ◽  
Liqing Cui ◽  
Changye Zhu ◽  
Nan Zhao ◽  
Baobin Li ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Well Performance ◽  
System Transformation ◽  
Gait Patterns ◽  
Quality Of Patient Care ◽  
Recognition Of Emotions

Emotion recognition can improve the quality of patient care, product development and human-machine interaction.Psychological studies indicate that emotional state can be expressed in the way people walk,and the human gait can be used to reveal a person's emotional state.This paper proposes a novel method to do emotion recognition by using Microsoft Kinect to record gait patterns and train machine learning algorithms for emotion recognition. 59 subjects are recruited, and their gait patterns are recorded by two Kinect cameras.Joint selection, coordinate system transformation, sliding window gauss filtering,differential operation, and data segmentation are used for data preprocessing.We run Fourier transformation to extract features from the gait patterns and utilize Principal Component Analysis(PCA) for feature selection. By using NaiveBayes, RandomForests, LibSVM and SMO classifiers, the accuracy of recognition between natural and angry emotions can reach 80%,and the accuracy of recognition between natural and happy emotions can reach above 70%.The result indicates that Kinect can be used in the recognition of emotions with fairly well performance.

scholarly journals Recognition of emotions using Kinects

2015 ◽  
Author(s):  
Shun Li ◽  
Liqing Cui ◽  
Changye Zhu ◽  
Nan Zhao ◽  
Baobin Li ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Well Performance ◽  
System Transformation ◽  
Gait Patterns ◽  
Quality Of Patient Care ◽  
Recognition Of Emotions

Emotion recognition can improve the quality of patient care, product development and human-machine interaction.Psychological studies indicate that emotional state can be expressed in the way people walk,and the human gait can be used to reveal a person's emotional state.This paper proposes a novel method to do emotion recognition by using Microsoft Kinect to record gait patterns and train machine learning algorithms for emotion recognition. 59 subjects are recruited, and their gait patterns are recorded by two Kinect cameras.Joint selection, coordinate system transformation, sliding window gauss filtering,differential operation, and data segmentation are used for data preprocessing.We run Fourier transformation to extract features from the gait patterns and utilize Principal Component Analysis(PCA) for feature selection. By using NaiveBayes, RandomForests, LibSVM and SMO classifiers, the accuracy of recognition between natural and angry emotions can reach 80%,and the accuracy of recognition between natural and happy emotions can reach above 70%.The result indicates that Kinect can be used in the recognition of emotions with fairly well performance.

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