scholarly journals Automatic Recognition Systems and Human Computer Interaction in Face Matching

2021 ◽  
pp. 193-215
Author(s):  
Eilidh Noyes ◽  
Matthew Q. Hill
Keyword(s):  
Face Recognition ◽  
Human Computer Interaction ◽  
Automatic Recognition ◽  
Image Similarity ◽  
Human Face ◽  
Identification Task ◽  
Face Matching ◽  
Recognition Systems ◽  
Computer Recognition ◽  
Automatic Systems

The human face facilitates identification in security and policing scenarios. In these settings, automatic face recognition systems have increased in prevalence and accuracy in recent years. As a result, the identification task, which once fell entirely to humans, is now a process performed by man and machine. Automatic face recognition systems provide image similarity comparisons and can create candidate lists to narrow down potential targets. There is increasing interest in the accuracy of these systems, and the role that algorithms can play in the identification effort. The design, operational usage, and effectiveness of these automatic systems, as well as the interaction of human and computer recognition are the topics of this chapter.

scholarly journals Surgical face masks impair human face matching performance for familiar and unfamiliar faces

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Daniel J. Carragher ◽  
Peter J. B. Hancock
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Human Face ◽  
Face Matching ◽  
Face Recognition System ◽  
Surgical Masks ◽  
The Face ◽  
Matching Performance ◽  
Recognition Systems ◽  
Unfamiliar Faces

AbstractIn response to the COVID-19 pandemic, many governments around the world now recommend, or require, that their citizens cover the lower half of their face in public. Consequently, many people now wear surgical face masks in public. We investigated whether surgical face masks affected the performance of human observers, and a state-of-the-art face recognition system, on tasks of perceptual face matching. Participants judged whether two simultaneously presented face photographs showed the same person or two different people. We superimposed images of surgical masks over the faces, creating three different mask conditions: control (no masks), mixed (one face wearing a mask), and masked (both faces wearing masks). We found that surgical face masks have a large detrimental effect on human face matching performance, and that the degree of impairment is the same regardless of whether one or both faces in each pair are masked. Surprisingly, this impairment is similar in size for both familiar and unfamiliar faces. When matching masked faces, human observers are biased to reject unfamiliar faces as “mismatches” and to accept familiar faces as “matches”. Finally, the face recognition system showed very high classification accuracy for control and masked stimuli, even though it had not been trained to recognise masked faces. However, accuracy fell markedly when one face was masked and the other was not. Our findings demonstrate that surgical face masks impair the ability of humans, and naïve face recognition systems, to perform perceptual face matching tasks. Identification decisions for masked faces should be treated with caution.

scholarly journals Surgical face masks impair human face matching performance for familiar and unfamiliar faces

2020 ◽  
Author(s):  
Daniel James Carragher ◽  
Peter Hancock
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Human Face ◽  
Face Matching ◽  
Face Recognition System ◽  
Surgical Masks ◽  
The Face ◽  
Matching Performance ◽  
Recognition Systems ◽  
Unfamiliar Faces

In response to the COVID-19 pandemic, many governments around the world now recommend, or require, that their citizens cover the lower half of their face in public. Consequently, many people now wear surgical face masks in public. We investigated whether surgical face masks affected the performance of human observers, and a state-of-the-art face recognition system, on tasks of perceptual face matching. Participants judged whether two simultaneously presented face photographs showed the same person or two different people. We superimposed images of surgical masks over the faces, creating three different mask conditions: control (no masks), mixed (one face wearing a mask), and masked (both faces wearing masks). We found that surgical face masks have a large detrimental effect on human face matching performance, and that the degree of impairment is the same regardless of whether one or both faces in each pair are masked. Surprisingly, this impairment is similar in size for both familiar and unfamiliar faces. When matching masked faces, human observers are biased to reject unfamiliar faces as “mismatches” and to accept familiar faces as “matches”. Finally, the face recognition system showed very high classification accuracy for control and masked stimuli, even though it had not been trained to recognise masked faces. However, accuracy fell markedly when one face was masked and the other was not. Our findings demonstrate that surgical face masks impair the ability of humans, and naïve face recognition systems, to perform perceptual face matching tasks. Identification decisions for masked faces should be treated with caution.

scholarly journals Convolutional neural net face recognition works in non-human-like ways

2020 ◽  
Vol 7 (10) ◽  
pp. 200595 ◽  
Author(s):  
Peter J. B. Hancock ◽  
Rosyl S. Somai ◽  
Viktoria R. Mileva
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
State Of The Art ◽  
Poor Performance ◽  
Neural Net ◽  
Human Face ◽  
Face Matching ◽  
Similarity Space ◽  
Recognition Systems ◽  
Human Participants

Convolutional neural networks (CNNs) give the state-of-the-art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising ‘errors'. We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face-matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to appear a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face-matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.

scholarly journals A Confidence-Based Update Rule for Self-updating Human Face Recognition Systems

2009 ◽  
pp. 151-160 ◽  
Author(s):  
Sri-Kaushik Pavani ◽  
Federico M. Sukno ◽  
Constantine Butakoff ◽  
Xavier Planes ◽  
Alejandro F. Frangi
Keyword(s):  
Face Recognition ◽  
Human Face ◽  
Human Face Recognition ◽  
Recognition Systems

scholarly journals An Elementary Study on Various Techniques Involved in Face Recognition Systems : A Review

2019 ◽  
pp. 519-525
Author(s):  
Payal Maken
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Digital Image ◽  
Salient Feature ◽  
Recognition System ◽  
Human Face ◽  
Detection Techniques ◽  
Face Recognition System ◽  
The Face ◽  
Recognition Systems

Face recognition has now become one of the interesting fields of research and has received a substantial attention of researchers from all over the world. Face recognition techniques has been mostly used in the discipline of image analysis, image processing, etc. One of the face recognition techniques is used to develop a face recognition system to detect a human face in an image. In face recognition system a digital image with a human face is given as an input which extracts the significant features of face such as (eyes, nose, chin, cheeks, etc) to recognize a face in a digital image which is an exhausting task. Security of information is very salient feature and is difficult to achieve. Security cameras are present in offices, universities, banks, ATMs, etc. All these security cameras are embedded with face recognition systems. There are various algorithms which are used to solve this problem. This paper provides an overview of various techniques which are often used for this face recognition in a face recognition system. This paper is divided into five parts, first section concludes various face detection techniques, second section describes about image processing ,third section have details about face recognition techniques, fourth section describes various classification methods and last section concludes all of these sections.

scholarly journals Comparison of Different Face Recognition Method Based On PCA

2014 ◽  
Vol 10 (4) ◽  
pp. 2016-2022
Author(s):  
Rajib Saha ◽  
Debotosh Bhattacharjee ◽  
Sayan Barman
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Manhattan Distance ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
Face Matching ◽  
Chebychev Distance

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.

Comparison of human face matching behavior and computational image similarity measure

2009 ◽  
Vol 52 (2) ◽  
pp. 316-321 ◽  
Author(s):  
WenFeng Chen ◽  
ChangHong Liu ◽  
Karen Lander ◽  
XiaoLan Fu
Keyword(s):  
Similarity Measure ◽  
Image Similarity ◽  
Human Face ◽  
Face Matching

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

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