scholarly journals Facial Recognition Patterns of Children and Adults Looking at Robotic Faces

10.5772/47836 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 28 ◽  
Author(s):  
Eunil Park ◽  
Ki Joon Kim ◽  
Angel P. del Pobil
Keyword(s):  
Facial Recognition ◽  
Eye Fixation ◽  
Adults And Children ◽  
Human Faces

The present study investigates whether adults and children exhibit different eye-fixation patterns when they look at human faces, machinelike robotic faces, and humanlike robotic faces. The results from two between-subject experiments showed that children and adults did have different facial recognition patterns; children tended to fixate more on the mouth of both machinelike and humanlike robotic faces than they do on human faces, while adults focused more on the eyes. The implications of notable findings and the limitations of the experiment are discussed.

Facial Affect Recognition in Schizophrenia is there a Differential Deficit?

1984 ◽  
Vol 144 (5) ◽  
pp. 533-537 ◽  
Author(s):  
Josh Novic ◽  
Daniel J. Luchins ◽  
Richard Perline
Keyword(s):  
Facial Recognition ◽  
Facial Affect ◽  
Affect Recognition ◽  
Facial Affect Recognition ◽  
Control Task ◽  
Test Items ◽  
Chronic Schizophrenics ◽  
Significant Deficit ◽  
Differential Deficit ◽  
Human Faces

SummarySeveral studies have suggested that schizophrenics have a deficit in their ability to recognize the affect expressed in photos of human faces. In this study, the performance of 17 chronic schizophrenics was compared to that of 17 controls on both a test of facial affect recognition and a control task involving facial recognition. Compared with controls, chronic schizophrenics tended to perform more poorly on the test of facial affect recognition, but this difference was eliminated when facial recognition was entered as a covariate. When all test items, including those with poor reliability and discriminatory power, were included in the analysis the schizophrenics showed a significant deficit in facial affect recognition which persisted even when facial recognition was used as a covariate.

scholarly journals Cloud Physiognomy

2016 ◽  
Vol 135 (1) ◽  
pp. 45-71 ◽  
Author(s):  
Lorraine Daston
Keyword(s):  
Nineteenth Century ◽  
Facial Recognition ◽  
Focused Attention ◽  
Standardize Nomenclature ◽  
Cloud Classification ◽  
Salient Features ◽  
Human Faces

After centuries of serving as the metaphor for mutability, clouds began to be classified by genera and species in the nineteenth century, on the model of Linnaean taxonomy. In order to standardize nomenclature, cloud watchers had to learn to see in unison, recognizing cloud types as one would recognize human faces. The analogy between cloud and facial recognition runs deep: in both cases, a few salient features (that aquiline nose, those long wispy streaks) are foregrounded at the expense of a great many others. What the art of caricature is to faces, condensed description was to clouds: a few bold strokes that focused attention on the essential and screened out everything else. Cloud classification depended crucially on description by omission.

Field Dependence and Facial Recognition

1977 ◽  
Vol 44 (1) ◽  
pp. 119-124 ◽  
Author(s):  
Curt Hoffman ◽  
Spencer Kagan
Keyword(s):  
Field Dependence ◽  
Facial Recognition ◽  
Embedded Figures Test ◽  
Field Independent ◽  
Field Dependent ◽  
Embedded Figures ◽  
Two Measures ◽  
Human Faces ◽  
Rod And Frame Test ◽  
Rod And Frame

29 male and 28 female undergraduates were administered two measures of Witkin's field-dependence dimension—the Portable Rod-and-frame Test and the Group Embedded-figures Test—and a test of facial recognition. Field-independent males were significantly more accurate in the recognition of photographed human faces than field-dependent males. Field-independent females were also more accurate than field-dependent females, although the relation was nonsignificant. While it has often been claimed that field-dependent individuals remember faces better, the results of the present study, as well as others which have examined this relationship, support the opposite conclusion.

scholarly journals Modelling of Facial Images for Analysis of Recognition System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012041
Author(s):  
Assyakirin M H ◽  
Shafriza Nisha B ◽  
Haniza Y ◽  
Fathinul Syahir A S ◽  
Muhammad Juhairi A S
Keyword(s):  
Face Recognition ◽  
Performance Analysis ◽  
Facial Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Background Intensity ◽  
Face Recognition System ◽  
Recognition Systems ◽  
Human Faces ◽  
Facial Images

Abstract Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.

Facial recognition, emotion and race in animated social media

First Monday ◽  
2018 ◽  
Author(s):  
Luke Stark
Keyword(s):  
Social Media ◽  
Emotional Expression ◽  
Facial Recognition ◽  
Smart Phones ◽  
Racial Identities ◽  
Video Sharing ◽  
Digital Animation ◽  
Identity Based ◽  
Recognition Systems ◽  
Human Faces

Facial recognition systems are increasingly common components of commercial smart phones such as the iPhone X and the Samsung Galaxy S9. These technologies are also increasingly being put to use in consumer-facing social media video-sharing applications, such as Apple’s animoji and memoji, Facebook Messenger’s masks and filters and Samsung’s AR Emoji. These animations serve as technical phenomena translating moments of affective and emotional expression into mediated socially legible forms. Through an analysis of these objects and the broader literature on digital animation, this paper critiques the ways these facial recognition systems classify and categorize racial identities in human faces. The paper considers the potential both for racializing logics as part of these systems of classification, and how data regarding emotional expression gathered through these systems might interact with identity-based forms of classification.

Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA)

2017 ◽  
Vol 31 (01) ◽  
pp. 1756001 ◽  
Author(s):  
L. G. Vu ◽  
Abeer Alsadoon ◽  
P. W. C. Prasad ◽  
A. M. S. Rahma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Background Removal ◽  
Hybrid Solution ◽  
Improving Accuracy ◽  
Human Faces ◽  
Color Conversion

Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have shown results when using a combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of a triangular algorithm in face normalization in order to enhance indexing accuracy. To evaluate the accuracy of the proposed hybrid indexing algorithm, the PCAaTA is tested and the results are compared with current solutions.

Stimulus Ratio Effects on Speech Discrimination by Children and Adults

1991 ◽  
Vol 34 (3) ◽  
pp. 671-678 ◽  
Author(s):  
Joan E. Sussman
Keyword(s):  
Discrimination Task ◽  
Response Pattern ◽  
Response Patterns ◽  
Speech Discrimination ◽  
Adult Group ◽  
Discrimination Accuracy ◽  
Formant Frequency ◽  
Response Strategies ◽  
Place Of Articulation ◽  
Adults And Children

This investigation examined the response strategies and discrimination accuracy of adults and children aged 5–10 as the ratio of same to different trials was varied across three conditions of a “change/no-change” discrimination task. The conditions varied as follows: (a) a ratio of one-third same to two-thirds different trials (33% same), (b) an equal ratio of same to different trials (50% same), and (c) a ratio of two-thirds same to one-third different trials (67% same). Stimuli were synthetic consonant-vowel syllables that changed along a place of articulation dimension by formant frequency transition. Results showed that all subjects changed their response strategies depending on the ratio of same-to-different trials. The most lax response pattern was observed for the 50% same condition, and the most conservative pattern was observed for the 67% same condition. Adult response patterns were most conservative across condition. Differences in discrimination accuracy as measured by P(C) were found, with the largest difference in the 5- to 6-year-old group and the smallest change in the adult group. These findings suggest that children’s response strategies, like those of adults, can be manipulated by changing the ratio of same-to-different trials. Furthermore, interpretation of sensitivity measures must be referenced to task variables such as the ratio of same-to-different trials.

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