scholarly journals Multimodal adaptive identity-recognition algorithm fused with gait perception

2021 ◽  
Vol 4 (4) ◽  
pp. 223-232
Author(s):  
Changjie Wang ◽  
Zhihua Li ◽  
Benjamin Sarpong
Keyword(s):  
Recognition Algorithm ◽  
Identity Recognition

scholarly journals Identity Recognition Algorithm Based on Static Gait Geometrical and Pressure Data

2018 ◽  
Vol 173 ◽  
pp. 03038
Author(s):  
Kun Wang ◽  
Yiwu Qian ◽  
Jinmo Wu ◽  
Xiaoyong Liu
Keyword(s):  
Gait Recognition ◽  
Recognition Algorithm ◽  
Pressure Data ◽  
Pattern Recognition Technique ◽  
Human Identity ◽  
Identity Recognition ◽  
Human Foot ◽  
Combined Features ◽  
Field Static ◽  
Recognition Technique

Currently, pattern recognition technique is widely applied in human identity recognition while there are shortages remaining in most kinds of such techniques. In order to overcome these problems, a novel algorithm is proposed to apply in identity recognition course in new field-static gait recognition field. Two combined features are going for static gait recognition: the distance between each part of center pressure and overall foot and the side length of outline triangle of human foot. Then through the comparison of different classifier, the best course for recognition can be obtained.

scholarly journals DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yingdong Wang ◽  
Qingfeng Wu ◽  
Chen Wang ◽  
Qunsheng Ruan
Keyword(s):  
Positive Emotions ◽  
Recognition Algorithm ◽  
Identification Accuracy ◽  
Identification System ◽  
Time Interval ◽  
Time Intervals ◽  
Identity Recognition ◽  
Eeg Data ◽  
Neutral Mood ◽  
The Impact

In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed. The performance of the proposed method is experimentally evaluated through the emotional EEG data. The conducted experiment shows that the proposed method approaches the stunning accuracy (ACC) of 99.7% on average and can rapidly train and update the DE-CNN model. Then, the effects of different emotions and the impact of different time intervals on the identification performance are investigated. Obtained results show that different emotions affect the identification accuracy, where the negative and neutral mood EEG has a better robustness than positive emotions. For a video signal as the EEG stimulant, it is found that the proposed method with 0–75 Hz is more robust than a single band, while the 15–32 Hz band presents overfitting and reduces the accuracy of the cross-emotion test. It is concluded that time interval reduces the accuracy and the 15–32 Hz band has the best compatibility in terms of the attenuation.

A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

2020 ◽  
Vol 13 (2) ◽  
pp. 207-221
Author(s):  
Minghua Wei
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Features ◽  
Recognition Algorithm ◽  
Residual Network ◽  
Content Type ◽  
Identity Recognition ◽  
Local Texture ◽  
Computation Efficiency ◽  
The Face

PurposeIn order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.Design/methodology/approachThe algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.FindingsCompared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/valueThe proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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