Use of Face Information Varies Systematically From Developmental Prosopagnosics to Super-Recognizers

2018 ◽  
Vol 30 (2) ◽  
pp. 300-308 ◽  
Author(s):  
Jessica Tardif ◽  
Xavier Morin Duchesne ◽  
Sarah Cohan ◽  
Jessica Royer ◽  
Caroline Blais ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Scales ◽  
Face Identification ◽  
Facial Features ◽  
Strongly Correlated ◽  
Identification Task ◽  
Spatial Frequencies ◽  
Recognition Ability ◽  
Neurologically Typical

Face-recognition abilities differ largely in the neurologically typical population. We examined how the use of information varies with face-recognition ability from developmental prosopagnosics to super-recognizers. Specifically, we investigated the use of facial features at different spatial scales in 112 individuals, including 5 developmental prosopagnosics and 8 super-recognizers, during an online famous-face-identification task using the bubbles method. We discovered that viewing of the eyes and mouth to identify faces at relatively high spatial frequencies is strongly correlated with face-recognition ability, evaluated from two independent measures. We also showed that the abilities of developmental prosopagnosics and super-recognizers are explained by a model that predicts face-recognition ability from the use of information built solely from participants with intermediate face-recognition abilities ( n = 99). This supports the hypothesis that the use of information varies quantitatively from developmental prosopagnosics to super-recognizers as a function of face-recognition ability.

scholarly journals The Goldsmiths Unfamiliar Face Memory and Before They Were Adult Tests

2020 ◽  
Author(s):  
Ashok Jansari ◽  
E. Green ◽  
Francesco Innocenti ◽  
Diego Nardi ◽  
Elena Belanova ◽  
...  
Keyword(s):  
Face Recognition ◽  
Holistic Processing ◽  
Face Identification ◽  
Facial Features ◽  
Face Memory ◽  
Face Matching ◽  
Recognition Ability ◽  
Unfamiliar Face ◽  
The Continuum

Unfamiliar face identification ability varies widely in the population. Those at the extreme top and bottom ends of the continuum have been labelled super-recognisers and prosopagnosics, respectively. Here we describe the development of two new tests - the Goldsmiths Unfamiliar Face Memory Test (GUFMT) and the Before They Were Adult Test (BTWA), that have been designed to measure different aspects of face identity ability across the spectrum. The GUFMT is a test of face memory, the BTWA a test of simultaneous adult-to-child face matching. Their designs draw on theories suggesting face identification is achieved by the recognition of facial features, the consistency across time of configurations between those features, and holistic processing of faces as a Gestalt. In four phases, participants (n = 16737), recruited using different methods, allowed evaluations to drive GUFMT development, the creation of likely population norms, as well as correlations with established face recognition tests. Recommendations for criteria for classification of super-recognition ability are also made.

A study of the face recognition ability of orthodontists and lay persons of different age groups

2012 ◽  
Vol 39 (1) ◽  
pp. 9-16
Author(s):  
Roz Walker ◽  
Mary Stokes ◽  
Michal Socker ◽  
Margaret Collins
Keyword(s):  
Face Recognition ◽  
Age Groups ◽  
Lay Persons ◽  
Recognition Ability ◽  
The Face

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Pre filtering techniques for face recognition based on edge detection algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 213
Author(s):  
Sheela Rani ◽  
Vuyyuru Tejaswi ◽  
Bonthu Rohitha ◽  
Bhimavarapu Akhil
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Facial Features ◽  
Human Face ◽  
Video Frames ◽  
Similar Idea ◽  
The Individual ◽  
The Given ◽  
Interesting Area

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

scholarly journals Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

2018 ◽  
Vol 7 (4) ◽  
pp. 9 ◽  
Author(s):  
Shakir F. Kak ◽  
Firas M. Mustafa ◽  
Pedro R. Valente
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Training Image ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Face Identification ◽  
Discrete Wavelet ◽  
Training Images ◽  
Matching Process ◽  
Cosine Distance

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

Newborns’ face recognition is based on spatial frequencies below 0.5 cycles per degree

Cognition ◽  
2008 ◽  
Vol 106 (1) ◽  
pp. 444-454 ◽  
Author(s):  
Adélaïde de Heering ◽  
Chiara Turati ◽  
Bruno Rossion ◽  
Hermann Bulf ◽  
Valérie Goffaux ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Frequencies
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