Robust Facial Recognition Using Extended Local Binary Patterns

2018 ◽  
Author(s):  
Gafour Yacine ◽  
Djamel Berrabah ◽  
Sidi Ahmed Mahmoudi
Keyword(s):  
Facial Recognition ◽  
Local Binary Patterns

scholarly journals Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1070-1077
Author(s):  
Li Wang ◽  
Ali Akbar Siddique
Keyword(s):  
Global Positioning System ◽  
Facial Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Positioning System ◽  
Complex Task ◽  
Global Positioning ◽  
The World ◽  
Facial Recognition System ◽  
Surveillance Application

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with. Street crimes and theft are the biggest threats for the citizens and their belonging. In order to provide security, there is an urgent need of a system that is capable of identifying the criminal in the crowded area. This paper proposes a facial recognition system using Local Binary Patterns Histogram Face recognizer mounted on drone technology. The facial recognition capability is a key feature for a drone to have in order to find or identify the person within the crowd. With the inception of drone technology in the proposed system, we can use it as a surveillance drone as well through which it can cover more area as compared to the stationary system. As soon as the system identifies the desired person, it tags him and transmits the image along with the co-ordinates of the location to the concerned authorities using mounted global positioning system. Proposed system is capable of identifying the person with the accuracy of approximately 89.1%.

scholarly journals Facial Expression Recognition with Appearance Based Features of Facial Landmarks

2021 ◽  
Vol 9 (VI) ◽  
pp. 3343-3348
Author(s):  
Kanaparthi Snehitha
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Facial Recognition ◽  
Local Binary Patterns ◽  
Interpersonal Relation ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Artificial Intelligence Technology ◽  
Facial Contours ◽  
Machine Interaction

Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like marketing, health care, behavioral analysis and also in human-machine interaction. Facial expression recognition helps a lot more than facial recognition. It helps the retailers to understand their customers, doctors to understand their patients, and organizations to understand their clients. For the expression recognition, we are using the landmarks of face which are appearance-based features. With the use of an active shape model, LBP (Local Binary Patterns) derives its properties from face landmarks. The operation is carried out by taking into account pixel values, which improves the rate of expression recognition. In an experiment done using previous methods and 10-fold cross validation, the accuracy achieved is 89.71%. CK+ Database is used to achieve this result.

scholarly journals Face recognition using viola-jones depending on python

2020 ◽  
Vol 20 (3) ◽  
pp. 1513
Author(s):  
Khansaa Dheyaa Ismael ◽  
Stanciu Irina
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Facial Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Security System ◽  
Software System ◽  
Human Face ◽  
Smart Buildings ◽  
Smart Building

<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

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