COMPLEX NETWORK PROPERTIES OF EYE-TRACKING IN THE FACE RECOGNITION PROCESS - An Initial Study

2012 ◽  
Keyword(s):  
Face Recognition ◽  
Eye Tracking ◽  
Complex Network ◽  
Initial Study ◽  
Recognition Process ◽  
The Face ◽  
Network Properties

scholarly journals Looking at my own Face: Visual Processing Strategies in Physical Self-representation

2016 ◽  
Author(s):  
Anya Chakraborty ◽  
Bhismadev Chakrabarti
Keyword(s):  
Face Recognition ◽  
Eye Tracking ◽  
Visual Processing ◽  
Recognition Task ◽  
Autistic Traits ◽  
Self And Other ◽  
Domain Specific ◽  
Processing Strategies ◽  
The Face ◽  
Specific Association

AbstractWe live in an age of ‘selfies’. Yet, how we look at our own faces has seldom been systematically investigated. In this study we test if visual processing of self-faces is different from other faces, using psychophysics and eye-tracking. Specifically, the association between the psychophysical properties of self-face representation and visual processing strategies involved in self-face recognition was tested. Thirty-three adults performed a self-face recognition task from a series of self-other face morphs with simultaneous eye-tracking. Participants were found to look at lower part of the face for longer duration for self-face compared to other-face. Participants with a reduced overlap between self and other face representations, as indexed by a steeper slope of the psychometric response curve for self-face recognition, spent a greater proportion of time looking at the upper regions of faces identified as self. Additionally, the association of autism-related traits with self-face processing metrics was tested, since autism has previously been associated with atypical self-processing, particularly in the psychological domain. Autistic traits were associated with reduced looking time to both self and other faces. However, no self-face specific association was noted with autistic traits, suggesting that autism-related features may be related to self-processing in a domain specific manner.

Show Me the Features! Understanding Recognition From the Use of Visual Information

2002 ◽  
Vol 13 (5) ◽  
pp. 402-409 ◽  
Author(s):  
Philippe G. Schyns ◽  
Lizann Bonnar ◽  
Frédéric Gosselin
Keyword(s):  
Face Recognition ◽  
Visual Information ◽  
Recognition Performance ◽  
New Method ◽  
Visual Categorization ◽  
Recognition Process ◽  
The Face ◽  
Memory Representations

We propose an approach that allows a rigorous understanding of the visual categorization and recognition process without asking direct questions about unobservable memory representations. Our approach builds on the selective use of visual information in recognition and a new method (Bubbles) to depict and measure what this information is. We examine three face-recognition tasks (identity, gender, expressive or not) and establish the componential and holistic information responsible for recognition performance. On the basis of this information, we derive task-specific gradients of probability for the allocation of attention to the different regions of the face.

scholarly journals Surveillance Camera using Face Recognition for Automatic Attendance Feeder and Energy Conservation in Classroom

2019 ◽  
Vol 8 (4) ◽  
pp. 3222-3225
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Energy Conservation ◽  
Electricity Supply ◽  
Recognition Process ◽  
Surveillance Camera ◽  
Surveillance Cameras ◽  
The Face

This works gives solution to two most important problems in the universities by equipping a surveillance camera with Artificial Intelligence (AI) technology. The first problem solved is unnecessary time wastage in manual and bio-metric (fingerprint based) attendance marking for students. The second problem solved is the unnecessary electricity wastage in classrooms without occupants. Using the videos getting recorded in surveillance cameras, the number of heads detection and face recognition is done. When there is no occupants in the class, the number of heads detected will be zero. So we can cut-off the electricity supply for that classroom. With the face recognition process the attendance for the students will be get automatically marked. The Intel movidius stick does the work of face recognition and finding the head counts.

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

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.

Face Recognition Process A Survey

2019 ◽  
Vol 7 (6) ◽  
pp. 999-1005
Author(s):  
Kavita Lodhi ◽  
Vandan Tewari ◽  
Priyanka Bamne
Keyword(s):  
Face Recognition ◽  
Recognition Process

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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