scholarly journals Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2799
Author(s):  
Koushik Roy ◽  
Md. Hasan ◽  
Labiba Rupty ◽  
Md. Sourave Hossain ◽  
Shirshajit Sengupta ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Electronic Devices ◽  
Fine Tuning ◽  
Classification Error ◽  
Biometric Authentication ◽  
Classification Error Rate ◽  
Feature Pyramid ◽  
Fourier Spectra ◽  
Biometric Based Authentication ◽  
Better Than

The emergence of biometric-based authentication using modern sensors on electronic devices has led to an escalated use of face recognition technologies. While these technologies may seem intriguing, they are accompanied by numerous implicit drawbacks. In this paper, we look into the problem of face anti-spoofing (FAS) on a frame level in an attempt to ameliorate the risks of face-spoofed attacks in biometric authentication processes. We employed a bi-directional feature pyramid network (BiFPN) that is used for convolutional multi-scaled feature extraction on the EfficientDet detection architecture, which is novel to the task of FAS. We further use these convolutional multi-scaled features in order to perform deep pixel-wise supervision. For all of our experiments, we performed evaluations across all major datasets and attained competitive results for the majority of the cases. Additionally, we showed that introducing an auxiliary self-supervision branch tasked with reconstructing the inputs in the frequency domain demonstrates an average classification error rate (ACER) of 2.92% on Protocol IV of the OULU-NPU dataset, which is significantly better than the currently available published works on pixel-wise face anti-spoofing. Moreover, following the procedures of prior works, we performed inter-dataset testing, which further consolidated the generalizability of the proposed models, as they showed optimum results across various sensors without any fine-tuning procedures.

FUZZY INTRINSIC MODE ENTROPY FOR DATA FEATURE EXTRACTION

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250009
Author(s):  
GEORGE S. TSOLIS ◽  
THOMAS D. XENOS
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Classification Error ◽  
Promising Technique ◽  
Mode Decomposition ◽  
Better Than

The notion of fuzzy entropy (FuzzyEn) is extended to the multiscale case by combining FuzzyEn and empirical mode decomposition (EMD). The proposed technique, fuzzy intrinsic entropy (FIMEn) performs better than its predecessor intrinsic monde entropy (IMEn) and it is less dependent on the algorithmic parameters. In a pattern recognition context, FIMEn provides more separable clusters than IMEn when used for feature extraction, thus allowing for less classification error. The above results suggest that the proposed multiscale entropy metric is a very promising technique for evaluating data regularity and can be used effectively for feature extraction in pattern recognition problems.

scholarly journals Electrical Impedance of Upper Limb Enables Robust Wearable Identity Recognition against Variation in Finger Placement and Environmental Factors

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 398
Author(s):  
Hyung Wook Noh ◽  
Joo Yong Sim ◽  
Chang-Geun Ahn ◽  
Yunseo Ku
Keyword(s):  
Upper Limb ◽  
Human Body ◽  
Structural Information ◽  
Real Life ◽  
The Body ◽  
Classification Error ◽  
Biometric Authentication ◽  
Classification Error Rate ◽  
Large Variability ◽  
Tissue Properties

Most biometric authentication technologies commercialized in various fields mainly rely on acquired images of structural information, such as fingerprints, irises, and faces. However, bio-recognition techniques using these existing physical features are always at risk of template forgery threats, such as fake fingerprints. Due to the risk of theft and duplication, studies have recently been attempted using the internal structure and biological characteristics of the human body, including our previous works on the ratiometric biological impedance feature. However, one may still question its accuracy in real-life use due to the artifacts from sensing position variability and electrode–skin interfacing noise. Moreover, since the finger possesses more severe thermoregulatory vasomotion and large variability in the tissue properties than the core of the body, it is necessary to mitigate the harsh changes occurring at the peripheral extremities of the human body. To address these challenges, we propose a biometric authentication method through robust feature extraction from the upper-limb impedance acquired based on a portable wearable device. In this work, we show that the upper limb impedance features obtained from wearable devices are robust against undesirable factors such as finger placement deviations and day-to-day physiological changes, along with ratiometric impedance features. Overall, our upper-limb impedance-based analysis in a dataset of 1627 measurement from 33 subjects lowered the classification error rate from 22.38% to 4.3% (by a factor of 5), and further down to 2.4% (by a factor of 9) when combined with the ratiometric features.

scholarly journals Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24 ◽  
Author(s):  
Fan Xu ◽  
Xin Shu ◽  
Xiaodi Zhang ◽  
Bo Fan
Keyword(s):  
Clustering Algorithm ◽  
Fitness Function ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Affinity Propagation ◽  
Cluster Center ◽  
Classification Error ◽  
Damping Factor ◽  
Classification Error Rate ◽  
Better Than

This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP) for bearing fault diagnosis automatically. First, SDAEs are used to extract potential fault features and directly reduce their high dimension to 3. To prove that the feature extraction capability of SDAEs is better than stacked autoencoders (SAEs), principal component analysis (PCA) is employed to compare and reduce their dimension to 3, except for the final hidden layer. Hence, the extracted 3-dimensional features are chosen as the input for adAP cluster models. Compared with other traditional cluster methods, such as the Fuzzy C-mean (FCM), Gustafson–Kessel (GK), Gath–Geva (GG), and affinity propagation (AP), clustering algorithms can identify fault samples without cluster center number selection. However, AP needs to set two key parameters depending on manual experience—the damping factor and the bias parameter—before its calculation. To overcome this drawback, adAP is introduced in this paper. The adAP clustering algorithm can find the available parameters according to the fitness function automatic. Finally, the experimental results prove that SDAEs with adAP are better than other models, including SDAE-FCM/GK/GG according to the cluster assess index (Silhouette) and the classification error rate.

A novel GNSS deformation feature extraction method based on ensemble improved LMD threshold denoising

2020 ◽  
Vol 14 (4) ◽  
pp. 445-453
Author(s):  
Qian Fan ◽  
Yiqun Zhu
Keyword(s):  
Feature Extraction ◽  
Spline Interpolation ◽  
Simulated Data ◽  
Mining Area ◽  
Deformation Monitoring ◽  
Denoising Method ◽  
Feature Extraction Method ◽  
Deformation Feature ◽  
Mode Decomposition ◽  
Better Than

AbstractIn order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.

Nature-inspired hybrid deep learning for race detection by face shape features

2020 ◽  
Vol 13 (3) ◽  
pp. 365-388
Author(s):  
Asha Sukumaran ◽  
Thomas Brindha
Keyword(s):  
Feature Extraction ◽  
Hybrid Algorithm ◽  
Shape Features ◽  
Error Performance ◽  
Color Feature ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Face Shape ◽  
Race Detection ◽  
Better Than

PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

Fingerprint Presentation Attack Detection Using Transfer Learning Approach

2021 ◽  
Vol 17 (1) ◽  
pp. 53-67
Author(s):  
Rajneesh Rani ◽  
Harpreet Singh
Keyword(s):  
Transfer Learning ◽  
Attack Detection ◽  
Classification Error ◽  
Learning Approach ◽  
Biometric Authentication ◽  
The Past ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Software And Hardware ◽  
Presentation Attack Detection

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

scholarly journals Dense-Res Net for Endoscopic Image Classification

2021 ◽  
Author(s):  
Quoc-Huy Trinh ◽  
Minh-Van Nguyen
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Anatomical Landmarks ◽  
Pathological Findings ◽  
Gi Tract ◽  
Target Class ◽  
Endoscopic Procedures ◽  
Endoscopic Image

We propose a method that configures Fine-tuning to a combination of backbone DenseNet and ResNet to classify eight classes showing anatomical landmarks, pathological findings, to endoscopic procedures in the GI tract. Our Technique depends on Transfer Learning which combines two backbones, DenseNet 121 and ResNet 101, to improve the performance of Feature Extraction for classifying the target class. After experiment and evaluating our work, we get accuracy with an F1 score of approximately 0.93 while training 80000 and test 4000 images.

scholarly journals Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mohammad J. M. Zedan ◽  
Ali I. Abduljabbar ◽  
Fahad Layth Malallah ◽  
Mustafa Ghanem Saeed
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Remote Control ◽  
Embedded Systems ◽  
Embedded System ◽  
Electronic Devices ◽  
Recognition Rate ◽  
Disabled People ◽  
Research Attention ◽  
Image Preprocessing

Nowadays, much research attention is focused on human–computer interaction (HCI), specifically in terms of biosignal, which has been recently used for the remote controlling to offer benefits especially for disabled people or protecting against contagions, such as coronavirus. In this paper, a biosignal type, namely, facial emotional signal, is proposed to control electronic devices remotely via emotional vision recognition. The objective is converting only two facial emotions: a smiling or nonsmiling vision signal captured by the camera into a remote control signal. The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. In terms of the smiling recognition, GENKl-4K database is exploited to train a model, which is built in the following sequenced steps: real-time video, snapshot image, preprocessing, face detection, feature extraction using HOG, and then finally SVM for the classification. The achieved recognition rate is up to 89% for the training and testing with 10-fold validation of SVM. In terms of IoT, the Arduino and MCU (Tx and Rx) nodes are exploited for transferring the resulting biosignal remotely as a server and client via the HTTP protocol. Promising experimental results are achieved by conducting experiments on 40 individuals who participated in controlling their emotional biosignals on several devices such as closing and opening a door and also turning the alarm on or off through Wi-Fi. The system implementing this research is developed in Matlab. It connects a webcam to Arduino and a MCU node as an embedded system.

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