Smart Surveillance System by Face Recognition and Tracking Using Machine Learning Techniques

2021 ◽  
pp. 1-14
Author(s):  
D. K. Niranjan ◽  
N. Rakesh
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Surveillance System ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Smart Surveillance ◽  
Smart Surveillance System

scholarly journals A Smart Survillience System

2020 ◽  
Vol 9 (1) ◽  
pp. 1135-1138
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Efficient Method ◽  
Surveillance System ◽  
Machine Learning Algorithms ◽  
Surveillance Video ◽  
Security Systems ◽  
Smart Surveillance ◽  
Human Faces ◽  
Smart Surveillance System

The smart surveillance system defines a approach to identify and recognize human faces from the surveillance videos. It is very tedious to find particular person within a video. This system gives a quick and efficient method to find the presence of a person within a surveillance video. The smart surveillance system uses various machine learning algorithms like Face Recognition and Face Detection to achieve the required results. This system can be used in many security systems to check the presence of a person in any video of the particular area.

scholarly journals Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques

2017 ◽  
Vol 5 ◽  
Author(s):  
Diego Montenegro Lopez ◽  
Flávio Luis de Mello ◽  
Cristina Maria Giordano Dias ◽  
Paula Almeida ◽  
Milton Araújo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surveillance System ◽  
Spotted Fever ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Skin based Occlusion Detection and Face Recognition using Machine Learning Techniques

2012 ◽  
Vol 41 (18) ◽  
pp. 11-15 ◽  
Author(s):  
G. SuvarnaKumar ◽  
P.V.G.D. Prasad Reddy ◽  
M. Srinadh Swamy ◽  
Sumit Gupta
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Machine Learning Techniques ◽  
Occlusion Detection ◽  
Learning Techniques

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.

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