The Face-Space Typicality Paradox: Understanding the Face-Space Metaphor

1998 ◽  
Vol 51 (3) ◽  
pp. 475-483 ◽  
Author(s):  
A. Mike Burton ◽  
John R. Vokey
Keyword(s):  
Face Processing ◽  
Dimensional Space ◽  
Short Note ◽  
High Dimensional ◽  
Human Face ◽  
Face Space ◽  
The Face

Some recent accounts of human face processing use the idea of “face space”, considered to be a multi-dimensional space whose dimensions correspond to ways in which faces can vary. Within this space, “typicality” is sometimes taken to reflect the proximity of a face to its local neighbours. Intuitions about the distribution of faces within the space may suggest that the majority of faces will be “typical” in these terms. However, when typicality measures are taken, researchers very rarely find that faces cluster at the “typical” end of the scale. In this short note we attempt to resolve this paradox and point out that reasoning about high dimensional distributions requires that some specific assumptions are made explicit.

Automated Attendance System Through Eigen Faces Using Image Processing

2016 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Meduri Sree Vidya
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Low Cost ◽  
Human Face ◽  
Face Space ◽  
Nodal Points ◽  
The Face ◽  
Low Dimensional

Eigen faces(PCA) approach for face recognition ,The face is an important part of who you are how people identify you. In face recognition there are two types of comparisons verification and identification. There are about 80 nodal points on a human face here are few nodal points that are measured by software that are distance between eyes ,width of the nose, Depth of the eye socket, Check bones, Jaw line and Chin By this method we can take automatic attendance .face recognition is done by projecting new image onto a low dimensional linear “face space” defined by the Eigen faces. This method is reliable , low cost , faster access and reduce man power.

scholarly journals FACE RECOGNITION THROUGH CASCADE CLASSIFIER USING EIGENFACES ALGORITHMS.

2020 ◽  
Vol 9 (6) ◽  
pp. 39-47
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Principal Component ◽  
Face Image ◽  
Human Face ◽  
Cascade Classifier ◽  
Facial Image ◽  
Human Face Recognition ◽  
Cascade Classifiers ◽  
The Face

The easiest way to distinguish each person's identity is through the face. Face recognition is included as an inevitable pre-processing step for face recognition. Face recognition itself has to face difficulties and challenges because sometimes some form of issue is quite different from human face recognition. There are two stages used for the human face recognition process, i.e. face detection, where this process is very fast in humans. In the first phase, the person stored the face image in the database from a different angle. The person's face image storage with the help of Eigenvector value depended on components - face coordinates, face index, face angles, eyes, nose, lips, and mouth within certain distances and positions with each other. There are two types of methods that are popular in currently developed face recognition patterns, the Cascade Classifier method and the Eigenface Algorithm. Facial image recognition The Eigenface method is based on the lack of dimensional space of the face, using principal component analysis for facial features. The main purpose of the use of cascade classifiers on facial recognition using the Eigenface Algorithm was made by finding the eigenvectors corresponding to the largest eigenvalues of the facial image

scholarly journals Signatures of personality on dense 3D facial images

2016 ◽  
Author(s):  
Sile Hu ◽  
Jieyi Xiong ◽  
Pengcheng Fu ◽  
Lu Qiao ◽  
Jingze Tan ◽  
...  
Keyword(s):  
Big Five ◽  
Image Data ◽  
Partial Least Square ◽  
Least Square ◽  
High Dimensional ◽  
Human Face ◽  
Big Five Model ◽  
The Face ◽  
Quantitative Analyses ◽  
Facial Images

AbstractIt has long been speculated that there exist cues on human face that allow observersto make reliable judgments of others’personality traits. However, direct evidences ofassociation between facial shapes and personality are missing. This study assessed thepersonality attributes for 834 Han Chinese volunteers (405 males and 429 females) utilizing the five-factor personality model (the ‘Big Five’ model), and collected their neutral 3D facial images. Dense anatomical correspondence was established across the 3D facial images to allow high-dimensional quantitative analyses on the face phenotypes. Two different approaches, Composite Partial Least Square Component(CPLSC) and principle component analysis (PCA) were used to test the associations between the self-testedpersonality scores and the dense 3D face image data. Among the fivepersonality factors, Agreeableness and Conscientiousness in male, and Extraversion in female were significantly associated to specific facial patterns. The personality-related facial patterns were extracted and their effects were extrapolated on simulated 3Dfacial models.

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.

Conventions and temporal differences in painted faces: A study of posture and color distribution

2020 ◽  
Vol 2020 (11) ◽  
pp. 267-1-267-8
Author(s):  
Mitchell J.P. van Zuijlen ◽  
Sylvia C. Pont ◽  
Maarten W.A. Wijntjes
Keyword(s):  
Scientific Community ◽  
Human Face ◽  
Art Galleries ◽  
Art Collections ◽  
Color Distribution ◽  
Scientific Domain ◽  
The Face ◽  
And Gender ◽  
Changes Over Time ◽  
Over Time

The human face is a popular motif in art and depictions of faces can be found throughout history in nearly every culture. Artists have mastered the depiction of faces after employing careful experimentation using the relatively limited means of paints and oils. Many of the results of these experimentations are now available to the scientific domain due to the digitization of large art collections. In this paper we study the depiction of the face throughout history. We used an automated facial detection network to detect a set of 11,659 faces in 15,534 predominately western artworks, from 6 international, digitized art galleries. We analyzed the pose and color of these faces and related those to changes over time and gender differences. We find a number of previously known conventions, such as the convention of depicting the left cheek for females and vice versa for males, as well as unknown conventions, such as the convention of females to be depicted looking slightly down. Our set of faces will be released to the scientific community for further study.

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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