Gait Period Detection Based on Layered Coding of Depth Information

Gait period detection, serving as a preprocessor for gait recognition, is commonly studied in the recent past. In this paper, we proposed a novel gait period detection method for depth gait video stream. The method introduces the concept of layered coding for depth images which decreases computational complexity. Furthermore, the extreme value of the sum of layered codes for gait sequence is utilized to judge the period endpoint, which is in accord with the naked-eye observation. In addition, gait recognition experiments on the TUM GAID database are conducted with the description of gait features of one single detected period by the proposed scheme using tensor representation. The high recognition accuracy verifies the effectiveness of the proposed depth gait period detection method.

Download Full-text

Extending Appearance Based Gait Recognition with Depth Data

Applied Sciences ◽

10.3390/app9245529 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5529

Author(s):

Kristijan Lenac ◽

Diego Sušanj ◽

Adnan Ramakić ◽

Domagoj Pinčić

Keyword(s):

Information Fusion ◽

Recognition Accuracy ◽

Gait Recognition ◽

Computational Cost ◽

Video Stream ◽

Depth Data ◽

Video Images ◽

Extraction Step ◽

Different Types ◽

Subject Identification

Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.

Download Full-text

Gait Recognition Based on the Feature Extraction of Gabor Filter and Linear Discriminant Analysis and Improved Local Coupled Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2020/5393058 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Hongli Guo ◽

Bin Li ◽

Youmei Zhang ◽

Yu Zhang ◽

Wei Li ◽

...

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Extreme Learning Machine ◽

Recognition Accuracy ◽

Gabor Filter ◽

Gait Recognition ◽

Effective Means ◽

Linear Discriminant ◽

Gait Features ◽

Learning Machine

A gait energy image contains much gait information, which is one of the most effective means to recognize gait characteristics. The accuracy of gait recognition is greatly affected by covariates, such as the viewing angle, occlusion of clothing, and walking speed. Gait features differ somewhat by angles. Therefore, how to improve the recognition accuracy of a cross-view gait is a challenging task. This study proposes a new gait recognition algorithm structure. A Gabor filter is used to extract gait features from gait energy images, since it can extract features of different directions and scales. We use linear discriminant analysis (LDA) to tackle the problem that the feature dimension restricts the process. Finally, the improved local coupled extreme learning machine based on particle swarm optimization is used for the classification process of the extracted features of the gait. The proposed method and other current mainstream algorithms are compared in terms of the recognition accuracy based on the CASIA-A and CASIA-B datasets, and the simulation results show that the proposed algorithm has good performance and performs well at cross-view gait recognition.

Download Full-text

An Identity Authentication Method Combining Liveness Detection and Face Recognition

Sensors ◽

10.3390/s19214733 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4733 ◽

Cited By ~ 5

Author(s):

Shuhua Liu ◽

Yu Song ◽

Mengyu Zhang ◽

Jianwei Zhao ◽

Shihao Yang ◽

...

Keyword(s):

Face Recognition ◽

Infrared Radiation ◽

Detection Method ◽

Recognition Accuracy ◽

Identity Authentication ◽

Depth Information ◽

Kinect Sensor ◽

Liveness Detection ◽

Kinect Camera ◽

Better Than

In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.

Download Full-text

Human gait recognition using orthogonal least square as feature selection

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i3.pp1355-1361 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1355

Author(s):

Rohilah Sahak ◽

Nooritawati Md Tahir ◽

Ahmad Ihsan Mohd Yassin ◽

Fadhlan Hafizhelmi Kamaruzaman

Keyword(s):

Recognition Accuracy ◽

Gait Recognition ◽

Least Square ◽

Human Gait ◽

Polynomial Kernel ◽

Oblique View ◽

3D Space ◽

Gait Features ◽

Rbf Kernel ◽

Orthogonal Least Square

<span>This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel.</span>

Download Full-text

A Lightweight Attention-Based CNN Model for Efficient Gait Recognition with Wearable IMU Sensors

Sensors ◽

10.3390/s21082866 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2866

Author(s):

Haohua Huang ◽

Pan Zhou ◽

Ye Li ◽

Fangmin Sun

Keyword(s):

Recognition Accuracy ◽

Recognition Performance ◽

Gait Recognition ◽

Wearable Sensors ◽

Model Complexity ◽

Model Parameters ◽

Softmax Classifier ◽

Proposed Model ◽

Comparison Results ◽

Gait Features

Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvement and become a new promising trend. However, most of the existing studies mainly focused on improving the gait recognition accuracy while ignored model complexity, which make them unsuitable for wearable devices. In this study, we proposed a lightweight attention-based Convolutional Neural Networks (CNN) model for wearable gait recognition. Specifically, a four-layer lightweight CNN was first employed to extract gait features. Then, a novel attention module based on contextual encoding information and depthwise separable convolution was designed and integrated into the lightweight CNN to enhance the extracted gait features and simplify the complexity of the model. Finally, the Softmax classifier was used for classification to realize gait recognition. We conducted comprehensive experiments to evaluate the performance of the proposed model on whuGait and OU-ISIR datasets. The effect of the proposed attention mechanisms, different data segmentation methods, and different attention mechanisms on gait recognition performance were studied and analyzed. The comparison results with the existing similar researches in terms of recognition accuracy and number of model parameters shown that our proposed model not only achieved a higher recognition performance but also reduced the model complexity by 86.5% on average.

Download Full-text

Machete

ACM Transactions on Computer-Human Interaction ◽

10.1145/3428068 ◽

2021 ◽

Vol 28 (1) ◽

pp. 1-46

Author(s):

Eugene M. Taranta II ◽

Corey R. Pittman ◽

Mehran Maghoumi ◽

Mykola Maslych ◽

Yasmine M. Moolenaar ◽

...

Keyword(s):

Video Game ◽

Recognition Accuracy ◽

Training Sample ◽

Low Latency ◽

Computationally Efficient ◽

Extensive Evaluation ◽

Segmentation Accuracy ◽

Alternative Techniques ◽

High Recognition Accuracy ◽

Continuous Dynamic

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.

Download Full-text

Ion-gating synaptic transistors with long-term synaptic weight modulation

Journal of Materials Chemistry C ◽

10.1039/d1tc00048a ◽

2021 ◽

Vol 9 (16) ◽

pp. 5396-5402

Author(s):

Youngjun Park ◽

Min-Kyu Kim ◽

Jang-Sik Lee

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Synaptic Weight ◽

Recognition Accuracy ◽

Artificial Neural ◽

High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

Download Full-text

Recognition of Symbolic Gestures Using Depth Information

Advances in Human-Computer Interaction ◽

10.1155/2018/1069823 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Hasan Mahmud ◽

Md. Kamrul Hasan ◽

Abdullah-Al-Tariq ◽

Md. Hasanul Kabir ◽

M. A. Mottalib

Keyword(s):

Contextual Information ◽

Depth Map ◽

Recognition System ◽

Support Vector ◽

Svm Classifier ◽

Depth Information ◽

Scale Invariant ◽

Binary Images ◽

Depth Images ◽

Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

Download Full-text

Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽

10.1155/2020/8873315 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dongsheng Wang ◽

Jun Feng ◽

Xinpeng Zhao ◽

Yeping Bai ◽

Yujie Wang ◽

...

Keyword(s):

Deep Learning ◽

Cement Mortar ◽

Recognition Accuracy ◽

Image Data ◽

Concrete Lining ◽

Learning Method ◽

Recognition Method ◽

Data Set ◽

Recognition Model ◽

High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

Download Full-text

The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes

Applied Sciences ◽

10.3390/app9173576 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3576 ◽

Cited By ~ 1

Author(s):

Yang ◽

Wang ◽

Yang

Keyword(s):

Lamb Wave ◽

Rapid Detection ◽

Detection Method ◽

Lamb Waves ◽

Thin Plates ◽

Depth Information ◽

Amplitude Curve ◽

Dispersion Characteristics ◽

Detection Technology ◽

Thin Walled

Thin-walled tubes are a kind of pressure vessel formed by a stamping and drawing process, which must withstand a great deal of sudden pressure during use. When microcrack defects of a certain depth are present on its inner and outer surfaces, severe safety accidents may occur, such as cracking and crushing. Therefore, it is necessary to carry out nondestructive testing of thin-walled tubes in the production process to eliminate the potential safety hazards. To realize the rapid detection of microcracks in thin-walled tubes, this study could be summarized as follows: (i) Because the diameters of the thin-walled tubes were much larger than their thicknesses, Lamb wave characteristics of plates with equal thicknesses were used to approximate the dispersion characteristics of thin-walled tubes. (ii) To study the dispersion characteristics of Lamb waves in thin plates, the detection method of the mode was determined using the particle displacement–amplitude curve. (iii) Using a multi-channel parallel detection method, rapid detection equipment for Lamb wave microcracks in thin-walled tubes was developed. (iv) The filtering peak values for defect signal detection with different depths showed that the defect detection peak values could reflect the defect depth information. (v) According to the minimum defect standard of a 0.045-mm depth, 100,000 thin-walled tubes were tested. The results showed that the missed detection rate was 0%, the reject rate was 0.3%, and the detection speed was 5.8 s/piece, which fully meets the actual detection requirements of production lines. Therefore, this study not only solved the practical issues for the rapid detection of microcracks in thin-walled tubes but also provided a reference for the application of ultrasonic technology for the detection of other components.

Download Full-text