A Survey based on Machine Learning Approaches for Detection of Human Behavioural Lie using physiological sensors and Face Recognition System

2018 ◽  
Vol 6 (11) ◽  
pp. 797-806
Author(s):  
Bishan Lal Thakur ◽  
Divyansh Thakur ◽  
Payal Pandey
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Recognition System ◽  
Learning Approaches ◽  
Face Recognition System ◽  
Physiological Sensors

scholarly journals Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4838
Author(s):  
Philip Gouverneur ◽  
Frédéric Li ◽  
Wacław M. Adamczyk ◽  
Tibor M. Szikszay ◽  
Kerstin Luedtke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Feature Learning ◽  
Recognition System ◽  
Extraction Methods ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Physiological Sensors ◽  
Pain Recognition

While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.

scholarly journals Real time Machine Learning Approach for a Smart Door Unlocking Using Face recognition System

2021 ◽  
Vol 1130 (1) ◽  
pp. 012080
Author(s):  
P Rayavel ◽  
A Sivakumar ◽  
P V Gopirajan ◽  
S Surenderanath ◽  
P Rathnavel
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Learning Approach ◽  
Time Machine ◽  
Face Recognition System ◽  
Machine Learning Approach

scholarly journals Face Recognition System with Machine Learning

2021 ◽  
Vol 2089 (1) ◽  
pp. 012047
Author(s):  
Vuppu Padmakar ◽  
B V Ramana Murthy
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Open Source ◽  
Programming Language ◽  
Recognition System ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Face Recognition System ◽  
Machine Learning Models

Abstract This venture plans to give improved security by enabling a client to realize who is actually getting to the framework utilizing facial acknowledgment. The framework enables just approved clients to get entrance. Python is a programming language utilized alongside Machine learning methods and an open source library which is utilized to configuration, construct and train Machine learning models. Interface component is additionally accommodated unapproved clients to enroll to obtain entrance with the earlier authorization from the Admin.

Performance evaluation of 2D face recognition techniques under image processing attacks

2018 ◽  
Vol 32 (19) ◽  
pp. 1850212 ◽  
Author(s):  
Sahil Sharma ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Face Recognition ◽  
Recognition System ◽  
Two Dimensions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques ◽  
Face Recognition System

Face recognition is a vastly researched topic in the field of computer vision. A lot of work have been done for facial recognition in two dimensions and three dimensions. The amount of work done with face recognition invariant of image processing attacks is very limited. This paper presents a total of three classes of image processing attacks on face recognition system, namely image enhancement attacks, geometric attacks and the image noise attacks. The well-known machine learning techniques have been used to train and test the face recognition system using two different databases namely Bosphorus Database and University of Milano Bicocca three-dimensional (3D) Face Database (UMBDB). Three classes of classification models, namely discriminant analysis, support vector machine and k-nearest neighbor along with ensemble techniques have been implemented. The significance of machine learning techniques has been mentioned. The visual verification has been done with multiple image processing attacks.

scholarly journals Classes of Unique Face and Its Classification

2013 ◽  
Vol 7 (1) ◽  
pp. 968-973
Author(s):  
Raghavendra Kulkarni ◽  
Dr. P. Nageswar Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Law Enforcement ◽  
Recognition System ◽  
Singular Value ◽  
Control Information ◽  
Face Recognition System ◽  
Number Of Faces

Near resembling faces ,Look alike faces, disguised faces and many more are todays challenges for researchers in the field of face recognition and these challenges become more serious in case of large facial Variations. Humans are able to identify reliably a large number of faces but a automated face recognition system must be face specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. This paper shows that how the unique face which is having a unique singular value per face under different variations is effectively classified and recognized.

scholarly journals Researching a machine learning algorithm for a face recognition system

2021 ◽  
pp. 10-12
Author(s):  
Serhii Yevseiev ◽  
Anna Goloskokova ◽  
Olexander Shmatko
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Video Sequence ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Human Face Recognition ◽  
Face Recognition System ◽  
Digital Fingerprint ◽  
The Impact

This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence. The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face. The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition. A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented. The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence. During the study, the following tasks were solved: – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials); – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points; – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code; – development of a match search module, that is, the module compares the faceprint with the database until a match is found

scholarly journals Robust Singular Value Decomposition Algorithm for Unique Faces

2018 ◽  
Vol 4 (2) ◽  
pp. 596-603
Author(s):  
Ibrahim Patel ◽  
Raghavendra Kulkarni ◽  
Dr.P. Nageswar Rao
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Law Enforcement ◽  
Social Interactions ◽  
Recognition System ◽  
Singular Value ◽  
Control Information ◽  
Face Recognition System ◽  
Value Decomposition ◽  
Image Transformations

It has been read and also seen by physical encounters that there found to be seven near resembling humans by appearance .Many a times one becomes confused with respect to identification of  such near resembling faces when one encounters them. The  recognition  of  familiar  faces  plays  a  fundamental  role  in  our  social interactions. Humans  are  able  to  identify  reliably  a  large  number  of  faces  and psychologists  are  interested  in  understanding  the  perceptual  and  cognitive mechanisms  at  the  base  of  the  face  recognition  process. As it is needed that an automated face recognition system should be faces specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. The paper shows that the most resembling faces can be recognized by having a unique value per face under different variations. Certain image transformations, such as intensity negation, strange viewpoint changes,  and  changes  in  lighting  direction  can  severely  disrupt  human  face recognition. It has been said again and again by research scholars that SVD algorithm is not good enough to classify faces under large variations but this paper proves that the SVD algorithm is most robust algorithm and can be proved effective in identifying faces under large variations as applicable to unique faces. This paper works on these aspects and tries to recognize the unique faces by applying optimized SVD algorithm.

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