scholarly journals Naturalistic Face Learning in Infants and Adults

2021 ◽  
pp. 095679762110306
Author(s):  
Xiaomei Zhou ◽  
Shruti Vyas ◽  
Jinbiao Ning ◽  
Margaret C. Moulson
Keyword(s):  
Face Recognition ◽  
Age Groups ◽  
Natural Variability ◽  
Facial Appearance ◽  
Viewing Angle ◽  
Face Region ◽  
The Face ◽  
Difficult Challenge ◽  
Attentional Mechanisms

Everyday face recognition presents a difficult challenge because faces vary naturally in appearance as a result of changes in lighting, expression, viewing angle, and hairstyle. We know little about how humans develop the ability to learn faces despite natural facial variability. In the current study, we provide the first examination of attentional mechanisms underlying adults’ and infants’ learning of naturally varying faces. Adults ( n = 48) and 6- to 12-month-old infants ( n = 48) viewed videos of models reading a storybook; the facial appearance of these models was either high or low in variability. Participants then viewed the learned face paired with a novel face. Infants showed adultlike prioritization of face over nonface regions; both age groups fixated the face region more in the high- than low-variability condition. Overall, however, infants showed less ability to resist contextual distractions during learning, which potentially contributed to their lack of discrimination between the learned and novel faces. Mechanisms underlying face learning across natural variability are discussed.

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

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

scholarly journals Efficient Masked Face Recognition Method during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Walid Hariri
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Efficient Solutions ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Face Region ◽  
The Face ◽  
To Come ◽  
Primary Focus ◽  
Fully Connected

Abstract The COVID-19 is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to discard the masked face region. Next, we apply pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

scholarly journals Efficient Masked Face Recognition Method during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Walid Hariri
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Efficient Solutions ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Face Region ◽  
The Face ◽  
To Come ◽  
Primary Focus ◽  
Fully Connected

Abstract The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes the face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning based features in order to address the problem of masked face recognition process. The first step is to discard the masked face region. Next, we apply a pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, MLP is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

Face Detection Based on Color Model and Grayscale Value Distribution

2011 ◽  
Vol 341-342 ◽  
pp. 535-539
Author(s):  
Ying Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Value Distribution ◽  
Color Model ◽  
Face Region ◽  
Skin Region ◽  
The Future ◽  
The Face

With the development of technology concerning face recognition, it has aroused great attentions from related scientists and is envisioned to be widely applied in various fields in the future. In this paper we are going to describe an effective way for detecting face in the statistic images. The proposed method will firstly detect face region via using the color model, which has been described in my previous paper [1] so as to locate the candidate pixels. Then we analyze the gray value distribution of candidate pixels to segment a more accurate region. Finally the face region is likely to be obtained after the exclusion of superfluous skin region.

A Method of Image Automatic Clipping Based on Face Recognition

2014 ◽  
Vol 543-547 ◽  
pp. 2531-2534
Author(s):  
Hong Wei Di ◽  
Cai Yun Wang
Keyword(s):  
Face Recognition ◽  
Relative Position ◽  
Experimental Results ◽  
Fixed Size ◽  
Face Area ◽  
Face Region ◽  
Template Image ◽  
The Face ◽  
Region Size

To solve the problem that traditional automatic image clipping method is on the basic of simple principles, such as fixed size and fixed location, an improved arithmetic based on face recognition is proposed. Firstly, the face region is located by face detecting.Then according to the proportion of face area in the selected region on the template image,the clipping region size of the image to cut is matched.At last,through the relative position of the face center in the template image,the cutting position can be got. The experimental results show that this algorithm can achieve better clipping effect.

scholarly journals Student Smart Attendance Through Face Recognition using Machine Learning Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 2348-2352
Keyword(s):  
Face Recognition ◽  
Learning Algorithm ◽  
Working Hours ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Cascade Algorithm ◽  
Face Region ◽  
The Face ◽  
Haar Cascade ◽  
Classroom Time

In today’s competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree Algorithm.

scholarly journals Effective Method of Age Dependent Face Recognition

2011 ◽  
pp. 125-129
Author(s):  
Hlaing Htake Khaung Tin ◽  
Myint Myint Sein
Keyword(s):  
Face Recognition ◽  
Age Groups ◽  
Recognition System ◽  
Input Image ◽  
Face Database ◽  
Face Representation ◽  
Face Space ◽  
Age Dependent ◽  
The Face ◽  
Age Prediction

This Automatic age dependent face recognition system is developed. This approach is based on the Principle Component Analysis (PCA). Eigen face approach is used for both age prediction and face recognition. Face database is created by aging groups individually. The age prediction is carried out by projecting a new face image into this face space and then comparing its position in the face space with those of known faces. After that we find the best match in the related face database, the Eigen face representation of an input image is first obtained. Then it is compared with the Eigen face representation of face in the database. The closest one is the match. It will be reduced the time complexity using this approach. The proposed method preserves the identity of the subject while enforcing a realistic recognition effects on adult facial images between 15 to 70 years old. The accuracy of the system is analyzed by the variation on the range of the age groups. The efficiency of the system can be confirmed through the experimental results.

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