scholarly journals Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7130
Author(s):  
Gyu Ho Choi ◽  
Hoon Ko ◽  
Witold Pedrycz ◽  
Amit Kumar Singh ◽  
Sung Bum Pan
Keyword(s):  
High Performance ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Recognition System ◽  
Normalization Method ◽  
Morphological Features ◽  
P Wave ◽  
Post Exercise ◽  
Exercise Ecg ◽  
User Recognition

Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

Mineral interpretation results using deep learning with hyperspectral imagery

2020 ◽  
Author(s):  
Andrés Bell ◽  
Carlos Roberto Del-Blanco ◽  
Fernando Jaureguizar ◽  
Narciso García ◽  
María José Jurado
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Raw Materials ◽  
Recognition Performance ◽  
Hyperspectral Imagery ◽  
Recognition System ◽  
Short Wave ◽  
Mineral Deposits ◽  
New Mineral ◽  
Recognition Capability

<p>Minerals are key resources for several industries, such as the manufacturing of high-performance components and the latest electronic devices. For the purpose of finding new mineral deposits, mineral interpretation is a task of great relevance in mining and metallurgy sectors. However, it is usually a long, costly, laborious, and manual procedure. It involves the characterization of mineral samples in laboratories far from the mineral deposits and it is subject to human interpretation mistakes. To address the previous problems, an automatic mineral recognition system is proposed that analyzes in real-time hyperspectral imagery acquired in different spectral ranges: VN-SWIR (Visible, Near and Short Wave Infrared) and LWIR (Long Wave Infrared). Thus, more efficient, faster, and more economic explorations are performed, by analyzing in-situ mineral deposits in the subsurface, instead of in laboratories. The developed system is based on a deep learning technique that implements a semantic segmentation neural network that considers spatial and spectral correlations. Two different databases composed by scanned drilled mineral cores from different mineral deposits have been used to evaluate the mineral interpretation capability. The first database contains hyperspectral images in the VN-SWIR range and the second one in the LWIR range. The obtained results show that the mineral recognition for the first database (VN-SWIR band) achieves an 86% in accuracy considering the following mineral classes: Actinolite, amphibole, biotite-chlorite, carbonate, epidote, saponite, whitemica and whitemica-chlorite. For the second database (LWIR band), a 90% in accuracy has been obtained with the following mineral classes: Albite, amphibole, apatite, carbonate, clinopyroxene, epidote, microcline, quartz, quartz-clay-feldspar and sulphide-oxide. The mineral recognition capability has been also compared between both spectral bands considering the common minerals in both databases. The results show a higher recognition performance in the LWIR band, achieving a 96% in accuracy, than in the VN-SWIR bands, which achieves an accuracy of 85%. However, the hyperspectral cameras covering VN-SWIR range are significantly more economic than those covering the LWIR range, and therefore making them a very interesting option for low-budget systems, but still with a good mineral recognition performance. On the other hand, there is a better recognition capability for those mineral categories with a higher number of samples in the databases, as expected. Acknowledgement: This research was funded the EIT Raw Materials through the Innovative geophysical logging tools for mineral exploration - 16350 InnoLOG Upscaling Project.</p>

scholarly journals 3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

2019 ◽  
Vol 19 (2) ◽  
pp. 28-37
Author(s):  
Hawraa H. Abbas ◽  
Bilal Z. Ahmed ◽  
Ahmed Kamil Abbas
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
3D Face Recognition ◽  
Shape Variation ◽  
3D Face ◽  
The Face

Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Fuzzy Logic System ◽  
Interpolation Scheme ◽  
Speech Pattern ◽  
Eigen Value ◽  
Target Speaker ◽  
Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

2020 ◽  
Author(s):  
Soma Nonaka ◽  
Kei Majima ◽  
Shuntaro C. Aoki ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
High Performance ◽  
Deep Neural Networks ◽  
Recognition Performance ◽  
Brain Activity ◽  
Spatial Integration ◽  
Hierarchical Processing ◽  
Visual Areas ◽  
The Brain

SummaryAchievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pretrained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

AUTHENTICATION OF FINGERPRINT BIOMETRICS ACQUIRED USING A CELLPHONE CAMERA: A REVIEW

2013 ◽  
Vol 11 (05) ◽  
pp. 1350033 ◽  
Author(s):  
MOHAMMED S. KHALIL ◽  
FAJRI KURNIAWAN ◽  
KASHIF SALEEM
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
High Performance ◽  
Personal Information ◽  
Current Trend ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Fingerprint Biometrics ◽  
Smart Mobile Phone ◽  
Smart Mobile

Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

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