scholarly journals Preferring and Detecting Face Symmetry: Comparing Children and Adults Judging Human and Monkey Faces

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2112
Author(s):  
Anthony C. Little ◽  
Jack A. F. Griffey
Keyword(s):  
Visual System ◽  
Age Groups ◽  
Perceptual Bias ◽  
Partner Choice ◽  
Human Face ◽  
Partner Preference ◽  
Selection Pressures ◽  
Evolutionary Selection ◽  
Evolutionary Advantage ◽  
Human Faces

Background: Visual symmetry is often found attractive. Symmetry may be preferred either due to a bias in the visual system or due to evolutionary selection pressures related to partner preference. Simple perceptual bias views predict that symmetry preferences should be similar across types of stimuli and unlikely to be related to factors such as age. Methods: The current study examined preferences for symmetry across age groups (pre-puberty vs post-puberty) and stimuli type (human face vs monkey face). Pairs of images manipulated for symmetry were presented and participants asked to choose the image they preferred. Participants repeated the task and were asked to detect symmetry. Results: Both age of observer and stimuli type were associated with symmetry preferences. Older observers had higher preferences for symmetry but preferred it most in human vs monkey stimuli. Across both age groups, symmetry preferences and detection abilities were weakly related. Conclusions: The study supports some ideas from an evolutionary advantage view of symmetry preference, whereby symmetry is expected be higher for potential partners (here human faces) and higher post-puberty when partner choice becomes more relevant. Such potentially motivational based preferences challenge perceptual bias explanations as a sole explanation for symmetry preferences but may occur alongside them.

Newborn's Preference for Faces

1996 ◽  
Vol 1 (3) ◽  
pp. 200-205 ◽  
Author(s):  
Carlo Umiltà ◽  
Francesca Simion ◽  
Eloisa Valenza
Keyword(s):  
Visual System ◽  
Structural Properties ◽  
Sensory Properties ◽  
Human Face ◽  
System Experiment ◽  
Face Experiment

Four experiments were aimed at elucidating some aspects of the preference for facelike patterns in newborns. Experiment 1 showed a preference for a stimulus whose components were located in the correct arrangement for a human face. Experiment 2 showed a preference for stimuli that had optimal sensory properties for the newborn visual system. Experiment 3 showed that babies directed their attention to a facelike pattern even when it was presented simultaneously with a non-facelike stimulus with optimal sensory properties. Experiment 4 showed the preference for facelike patterns in the temporal hemifield but not in the nasal hemifield. It was concluded that newborns' preference for facelike patterns reflects the activity of a subcortical system which is sensitive to the structural properties of the stimulus.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

scholarly journals Typical emotional expression in children’s drawings of the human face

2021 ◽  
Author(s):  
Eleonora Cannoni ◽  
Giuliana Pinto ◽  
Anna Silvia Bombi
Keyword(s):  
Emotional Expression ◽  
Visual Cues ◽  
Age Groups ◽  
Emotional Expressions ◽  
Basic Emotions ◽  
Preferential Expression ◽  
Human Figure ◽  
Figure Drawings ◽  
Number Of Faces ◽  
Human Faces

AbstractThis study was aimed at verifying if children introduce emotional expressions in their drawings of human faces, and if a preferential expression exists; we also wanted to verify if children’s pictorial choices change with increasing age. To this end we examined the human figure drawings made by 160 boys and 160 girls, equally divided in 4 age groups: 6–7; 8–9; 10–11; 12–13 years; mean ages (SD in parentheses) were: 83,30 (6,54); 106,14 (7,16) 130,49 (8,26); 155,40 (6,66). Drawings were collected with the Draw-a-Man test instructions, i.e. without mentioning an emotional characterization. In the light of data from previous studies of emotion drawing on request, and the literature about preferred emotional expressions, we expected that an emotion would be portrayed even by the younger participants, and that the preferred emotion would be happiness. We also expected that with the improving ability to keep into account both mouth and eyes appearance, other expressions would be found besides the smiling face. Data were submitted to non-parametric tests to compare the frequencies of expressions (absolute and by age) and the frequencies of visual cues (absolute and by age and expressions). The results confirmed that only a small number of faces were expressionless, and that the most frequent emotion was happiness. However, with increasing age this representation gave way to a variety of basic emotions (sadness, fear, anger, surprise), whose representation may depend from the ability to modify the shapes of both eyes and mouth and changing communicative aims of the child.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

scholarly journals Smart Door / COVID-19 Face Mask Detection

2021 ◽  
Vol 10 (9) ◽  
pp. 87-92
Author(s):  
Pavan Narayana A ◽  
◽  
Janardhan Guptha S ◽  
Deepak S ◽  
Pujith Sai P ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Open Source ◽  
Face Mask ◽  
Young Lady ◽  
Grayscale Image ◽  
Human Face ◽  
Happy Life ◽  
Public Areas ◽  
The Face ◽  
Human Faces

January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.

Face Recognition with Active Appearance Model (AAM)

2013 ◽  
pp. 1124-1144 ◽  
Author(s):  
Patrycia Barros de Lima Klavdianos ◽  
Lourdes Mattos Brasil ◽  
Jairo Simão Santana Melo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Face Recognition ◽  
Face Processing ◽  
Research Field ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Human Face ◽  
Active Appearance ◽  
Human Faces

Recognition of human faces has been a fascinating subject in research field for many years. It is considered a multidisciplinary field because it includes understanding different domains such as psychology, neuroscience, computer vision, artificial intelligence, mathematics, and many others. Human face perception is intriguing and draws our attention because we accomplish the task so well that we hope to one day witness a machine performing the same task in a similar or better way. This chapter aims to provide a systematic and practical approach regarding to one of the most current techniques applied on face recognition, known as AAM (Active Appearance Model). AAM method is addressed considering 2D face processing only. This chapter doesn’t cover the entire theme, but offers to the reader the necessary tools to construct a consistent and productive pathway toward this involving subject.

scholarly journals BMD Loci Contribute to Ethnic and Developmental Differences in Skeletal Fragility across Populations: Assessment of Evolutionary Selection Pressures

2015 ◽  
Vol 32 (11) ◽  
pp. 2961-2972 ◽  
Author(s):  
Carolina Medina-Gómez ◽  
Alessandra Chesi ◽  
Denise H.M. Heppe ◽  
Babette S. Zemel ◽  
Jia-Lian Yin ◽  
...  
Keyword(s):  
Developmental Differences ◽  
Selection Pressures ◽  
Skeletal Fragility ◽  
Evolutionary Selection

scholarly journals A Study of Facial Features of American and Japanese Cartoon Characters

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 664 ◽  
Author(s):  
Kun Liu ◽  
Jun-Hong Chen ◽  
Kang-Ming Chang
Keyword(s):  
Design Process ◽  
Facial Features ◽  
Human Face ◽  
Research Results ◽  
Real People ◽  
Study Results ◽  
Animated Characters ◽  
Cartoon Characters ◽  
Human Faces ◽  
Japanese Animation

Many researchers think that the characters in animated cartoons and comics are designed according to the exaggeration or reduction of some features based on the human face. However, the feature distribution of the human face is relatively symmetrical and uniform. Thus, to ensure the characters look exaggerated, but without breaking the principle of symmetry, some questions remain: Which facial features should be exaggerated during the design process? How exaggerated are the faces of cartoon characters compared to real faces? To answer these questions, we selected 100 cartoon characters from American and Japanese animation, collected data from their facial features and the facial features of real people, and then described the features using angles, lengths, and areas. Finally, we compared cartoon characters’ facial features values with real facial features and determined the key parts and degree of facial exaggeration of animated characters. The research results show that American and Japanese cartoon characters both exaggerate the eyes, nose, ears, forehead, and chin. Compared with human faces, taking the eye area as an example, American animation characters are twice as large compared with human faces, whereas Japanese animation characters are 3.4 times larger than human faces. The study results can be used for reference by animation character designers and researchers.

3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

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