scholarly journals Soft biometrics: a survey

2021 ◽  
Author(s):  
Bilal Hassan ◽  
Ebroul Izquierdo ◽  
Tomas Piatrik
Keyword(s):  
Recognition System ◽  
Data Sets ◽  
Research Directions ◽  
Soft Biometrics ◽  
Multi Scale ◽  
Person Following ◽  
Real World Applications ◽  
New Research ◽  
Biometrics Recognition ◽  
Level Fusion

AbstractThe field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

scholarly journals Multi-Modal Iris Recognition System based on Convolution Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 798-803
Keyword(s):  
Neural Network ◽  
Confidence Level ◽  
Iris Recognition ◽  
Recognition System ◽  
Data Sets ◽  
Fusion Method ◽  
Softmax Classifier ◽  
Discriminative Property ◽  
Multi Class Classification ◽  
Level Fusion

Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.

scholarly journals Soft Biometrics and Deep Learning: Detecting Facial Soft Biometrics Features Using Ocular and Forehead Region for Masked Face Images

2022 ◽  
Author(s):  
Urja Banati ◽  
Vamika Prakash ◽  
Rashi Verma ◽  
Smriti Srivast
Keyword(s):  
Transfer Learning ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Data Sets ◽  
Soft Biometrics ◽  
Learning Techniques ◽  
Hair Type ◽  
The Face ◽  
And Gender ◽  
Improving Accuracy

Abstract Soft Biometrics is a growing field that has been known to improve the recognition system as witnessed in the past decade. When combined with hard biometrics like iris, gait, fingerprint recognition etc. it has been seen that the efficiency of the system increases many folds. With the Pandemic came the need to recognise faces covered with mask in an efficient way- soft biometrics proved to be an aid in this. While recent advances in computer vision have helped in the estimation of age and gender - the system could be improved by extending the scope and detecting quite a few other soft biometric attributes that helps us in identifying a person, including but not limited to - eyeglasses, hair type and color, mustache, eyebrows etc. In this paper we propose a system of identification that uses the ocular and forehead part of the face as modalities to train our models that uses transfer learning techniques to help in the detection of 12 soft biometric attributes (FFHQ dataset) and 25 soft biometric attributes (CelebA dataset) for masked faces. We compare the results with the unmasked faces in order to see the variation of efficiency using these data-sets Throughout the paper we have implemented 4 enhanced models namely - enhanced Alexnet ,enhanced Resnet50, enhanced MobilenetV2 and enhanced SqueezeNet. The enhanced models apply transfer learning to the normal models and aids in improving accuracy. In the end we compare the results and see how the accuracy varies according to the model used and whether the images are masked or unmasked. We conclude that for images containing facial masks - using enhanced MobileNet would give a splendid accuracy of 92.5% (for FFHQ dataset) and 87% (for CelebA dataset).

APPLICATION OF COMPETITIVE HOPFIELD NEURAL NETWORK TO BRAIN-COMPUTER INTERFACE SYSTEMS

2012 ◽  
Vol 22 (01) ◽  
pp. 51-62 ◽  
Author(s):  
WEI-YEN HSU
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Recognition System ◽  
Data Sets ◽  
Self Organizing Map ◽  
Time Frequency ◽  
Multi Scale ◽  
Eeg Data ◽  
Supervised Classifiers ◽  
Active Segment

We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.

scholarly journals A Comparative Study of Machine Learning Methods for Persistence Diagrams

2021 ◽  
Vol 4 ◽  
Author(s):  
Danielle Barnes ◽  
Luis Polanco ◽  
Jose A. Perea
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Shape Matching ◽  
Data Sets ◽  
Learning Methods ◽  
Data Set ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Real World Applications ◽  
Persistence Diagrams

Many and varied methods currently exist for featurization, which is the process of mapping persistence diagrams to Euclidean space, with the goal of maximally preserving structure. However, and to our knowledge, there are presently no methodical comparisons of existing approaches, nor a standardized collection of test data sets. This paper provides a comparative study of several such methods. In particular, we review, evaluate, and compare the stable multi-scale kernel, persistence landscapes, persistence images, the ring of algebraic functions, template functions, and adaptive template systems. Using these approaches for feature extraction, we apply and compare popular machine learning methods on five data sets: MNIST, Shape retrieval of non-rigid 3D Human Models (SHREC14), extracts from the Protein Classification Benchmark Collection (Protein), MPEG7 shape matching, and HAM10000 skin lesion data set. These data sets are commonly used in the above methods for featurization, and we use them to evaluate predictive utility in real-world applications.

INNOVATION CAPABILITY, ABSORPTIVE CAPACITY AND SMES PERFORMANCE IN PAKISTAN: THE MODERATING EFFECT OF BUSINESS STRATEGY

2018 ◽  
Vol 13 (Number 2) ◽  
pp. 1-11
Author(s):  
Muhammad Zulqarnain Arshad ◽  
Darwina Arshad
Keyword(s):  
Economic Growth ◽  
Absorptive Capacity ◽  
Business Strategy ◽  
Innovation Capability ◽  
Research Directions ◽  
Conceptual Paper ◽  
And Performance ◽  
New Research ◽  
The Relationship ◽  
Crucial Part

The small and medium-sized enterprises (SMEs) play a crucial part in county’s economic growth and a key contributor in country’s GDP. In Pakistan SMEs hold about 90 percent of the total businesses. The performance of SMEs depends upon many factors. The main aim for the research is to examine the relationship between Innovation Capability, Absorptive Capacity and Performance of SMEs in Pakistan. This conceptual paper also extends to the vague revelation on Business Strategy in which act as a moderator between Innovation Capability, Absorptive Capacity and SMEs Performance. Conclusively, this study proposes a new research directions and hypotheses development to examine the relationship among the variables in Pakistan’s SMEs context.

MATERIAL HANDLING MACHINES AND SYSTEMS – UMI-TWINN PROJECT CONTRIBUTION

2019 ◽  
Vol 12 (1) ◽  
pp. 7-20
Author(s):  
Péter Telek ◽  
Béla Illés ◽  
Christian Landschützer ◽  
Fabian Schenk ◽  
Flavien Massi
Keyword(s):  
Material Handling ◽  
Digital Data ◽  
Flow Process ◽  
Research Directions ◽  
Scientific Excellence ◽  
Research Activities ◽  
New Research ◽  
Eu Project ◽  
The University

Nowadays, the Industry 4.0 concept affects every area of the industrial, economic, social and personal sectors. The most significant changings are the automation and the digitalization. This is also true for the material handling processes, where the handling systems use more and more automated machines; planning, operation and optimization of different logistic processes are based on many digital data collected from the material flow process. However, new methods and devices require new solutions which define new research directions. In this paper we describe the state of the art of the material handling researches and draw the role of the UMi-TWINN partner institutes in these fields. As a result of this H2020 EU project, scientific excellence of the University of Miskolc can be increased and new research activities will be started.

scholarly journals Antimicrobial and Amyloidogenic Activity of Peptides Synthesized on the Basis of the Ribosomal S1 Protein from Thermus Thermophilus

2020 ◽  
Vol 21 (17) ◽  
pp. 6382 ◽  
Author(s):  
Stanislav Kurpe ◽  
Sergei Grishin ◽  
Alexey Surin ◽  
Olga Selivanova ◽  
Roman Fadeev ◽  
...  
Keyword(s):  
Antimicrobial Activity ◽  
Thermus Thermophilus ◽  
Antibacterial Properties ◽  
Cell Penetration ◽  
Research Directions ◽  
Bacterial Proteins ◽  
Hiv Transcription ◽  
Parent Protein ◽  
Amyloidogenic Peptides ◽  
New Research

Controlling the aggregation of vital bacterial proteins could be one of the new research directions and form the basis for the search and development of antibacterial drugs with targeted action. Such approach may be considered as an alternative one to antibiotics. Amyloidogenic regions can, like antibacterial peptides, interact with the “parent” protein, for example, ribosomal S1 protein (specific only for bacteria), and interfere with its functioning. The aim of the work was to search for peptides based on the ribosomal S1 protein from T. thermophilus, exhibiting both aggregation and antibacterial properties. The biological system of the response of Gram-negative bacteria T. thermophilus to the action of peptides was characterized. Among the seven studied peptides, designed based on the S1 protein sequence, the R23I (modified by the addition of HIV transcription factor fragment for bacterial cell penetration), R23T (modified), and V10I (unmodified) peptides have biological activity that inhibits the growth of T. thermophilus cells, that is, they have antimicrobial activity. But, only the R23I peptide had the most pronounced activity comparable with the commercial antibiotics. We have compared the proteome of peptide-treated and intact T. thermophilus cells. These important data indicate a decrease in the level of energy metabolism and anabolic processes, including the processes of biosynthesis of proteins and nucleic acids. Under the action of 20 and 50 μg/mL R23I, a decrease in the number of proteins in T. thermophilus cells was observed and S1 ribosomal protein was absent. The obtained results are important for understanding the mechanism of amyloidogenic peptides with antimicrobial activity and can be used to develop new and improved analogues.

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