Routes through the Face Recognition System

1991 ◽  
Vol 43 (4) ◽  
pp. 761-791 ◽  
Author(s):  
Dennis C. Hay ◽  
Andrew W. Young ◽  
Andrew W. Ellis
Keyword(s):  
Face Recognition ◽  
Semantic Information ◽  
Strong Support ◽  
Recognition System ◽  
Face Recognition System ◽  
The Face ◽  
Sequential Models ◽  
Recognition Errors ◽  
Unfamiliar Faces ◽  
Selection Of

Two experiments are reported which seek to examine the proposition first put forward by Hay and Young (1982), that recognition of a known person after seeing his or her face proceeds through a series of sequentially organized stages. In both experiments subjects were shown a selection of famous and unfamiliar faces and required to state whether each face was familiar. They were then asked to recall semantic information and the person's name. Of all the possible response types, only some are predicted by models derived from Hay and Young (1982), and only these responses were observed in Experiment 1. In order to give as complete an account as possible of the slips and errors made by subjects, they were interrogated some days after completing the testing phase in Experiment 2. As in the first experiment, the results supported the view that distinct but successive stages are involved in everyday face recognition. The method developed here provides an extension of the “diary” type of study of everyday recognition errors into laboratory conditions, which confirms the findings of studies of everyday errors and provides strong support for sequential models.

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.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals A FACIAL RECOGNITION USING OPEN COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.C K Gomathy ◽  
T. suneel ◽  
Y.Jeeevan Kumar Reddy
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Functional Space ◽  
Recognition System ◽  
Statistical Technique ◽  
Exciting Field ◽  
Two Dimensional Image ◽  
Face Recognition System ◽  
The Face ◽  
Amount Of Knowledge

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

A TIED-MIXTURE 2D HMM FACIAL IMAGE RETRIEVAL SYSTEM

2004 ◽  
Vol 13 (05) ◽  
pp. 1133-1146
Author(s):  
H. OTHMAN ◽  
T. ABOULNASR
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Training Data ◽  
Expected Improvement ◽  
Model Case ◽  
Face Recognition System ◽  
Image Retrieval System ◽  
The Face ◽  
Institute Of Technology ◽  
Observation Space

In this paper, the effect of mixture tying on a second-order 2D Hidden Markov Model (HMM) is studied as applied to the face recognition problem. While tying HMM parameters is a well-known solution in the case of insufficient training data that leads to nonrobust estimation, it is used here to improve the overall performance in the small model case where the resolution in the observation space is the main problem. The fully-tied-mixture 2D HMM-based face recognition system is applied to the facial database of AT&T and the facial database of Georgia Institute of Technology. The performance of the proposed 2D HMM tied-mixture system is studied and the expected improvement is confirmed.

scholarly journals A Web-Based Mobile Attendance System with Facial Recognition Feature

2020 ◽  
Vol 14 (05) ◽  
pp. 193
Author(s):  
Noradila Nordin ◽  
Nurul Husna Mohd Fauzi
Keyword(s):  
Face Recognition ◽  
Mobile Devices ◽  
Conventional Method ◽  
Facial Recognition ◽  
Recognition System ◽  
Web Based ◽  
Face Recognition System ◽  
The Face ◽  
System Prototype

Attendance marking in a classroom is one of the methods used to track the student’s presence in the lecture. The conventional method that is being enforced has shown to be vulnerable, inaccurate and time-consuming especially in a large classroom. It is difficult to identify absentees and proxy attendees based on the conventional attendance marking method. In order to overcome the challenges faced in the conventional method, a web-based mobile attendance system with facial recognition feature is proposed. It incorporated the existing mobile devices with a camera and the face recognition system to allow the attendance system to be used in classrooms automatically and efficiently with minor implementation requirements. The system prototype received positive responses from the volunteers who tested the system to replace the conventional attendance marking.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

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