scholarly journals Critical Features for Face Recognition

2018 ◽  
Author(s):  
Naphtali Abudarham ◽  
Lior Shkiller ◽  
Galit Yovel
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Recognition Algorithm ◽  
Facial Features ◽  
Perceptual Representation ◽  
Familiar Face ◽  
Perceptual Representations ◽  
Unfamiliar Faces ◽  
New Framework

Face recognition is a computationally challenging task that humans perform effortlessly. Nonetheless, this remarkable ability is limited to familiar faces and does not generalize to unfamiliar faces. To account for humans’ superior ability to recognize familiar faces, current theories suggest that familiar and unfamiliar faces have different perceptual representations. In the current study, we applied a reverse engineering approach to reveal which facial features are critical for familiar face recognition. In contrast to current views, we discovered that the same subset of features that are used for matching unfamiliar faces, are also used for matching as well as recognition of familiar faces. We further show that these features are also used by a deep neural network face recognition algorithm. We therefore propose a new framework that assumes similar perceptual representation for all faces and integrates cognition and perception to account for humans’ superior recognition of familiar faces.

A mask recognition algorithm based on improved YOLO-V4 neural network (Preprint)

2021 ◽  
Author(s):  
Song Zhang ◽  
Shaoqiang Wang ◽  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Target Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Prevention And Control ◽  
Preventive Measures ◽  
Recognition Algorithm ◽  
Facial Features ◽  
And Control

BACKGROUND With the spread of the new crown virus, the wearing of masks as one of the effective preventive measures is getting more and more attention, and the behavior of not wearing a mask is likely to cause the spread of the virus, which is not conducive to the prevention and control of the epidemic. OBJECTIVE In this paper, a new neural network model is used to better recognize the facial features of people with exit masks. METHODS This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network that can solve this problem well. This paper integrates SE-Net and DenseNet network as the reference neural network of YOLO-V4 and introduces deformable convolution. RESULTS Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of mask detection to a certain extent. CONCLUSIONS The improved YOLO-V4 network proposed in this article has verified its feasibility and accuracy through experiments and has great value in use. Improving the YOLO-V4 network can help better respond to face recognition with masks in the epidemic. However, the model studied in this article focuses on accuracy and is slightly lacking in speed. The next step is to increase its speed based on ensuring accuracy and consider actual deployment and use.

scholarly journals A Deep Neural Network for face Recognition

2019 ◽  
Vol 8 (12S) ◽  
pp. 420-423
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Facial Features ◽  
Biometric Authentication ◽  
Accuracy Rate ◽  
Learning Technique ◽  
The Face ◽  
Deep Learning Model

Face recognition is used to biometric authentication method to analyze the face extract and photographs useful to reputation formation from them, which can be usually called as a characteristic vector this is used to differentiate the organic features. In this paper to detect the suspect by extracting facial features from the captured image of the suspect from CCTV and match it with the pictures stored in the database and also to achieve an accuracy rate of 100 %, negligible loss using deep learning technique. For extracting the facial features, we are using deep learning model known as Convolutional Neural Network (CNN). It is one of the best models to extract features with the highest accuracy rate .

scholarly journals Face identity selectivity in the deep neural network and human brain

2021 ◽  
Author(s):  
Jinge Wang ◽  
Runnan Cao ◽  
Nicholas J Brandmeir ◽  
Xin Li ◽  
Shuo Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Human Brain ◽  
Face Processing ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Facial Features ◽  
Artificial Neurons ◽  
Human Neurons ◽  
Shed Light

A central challenge in face perception research is to understand how neurons encode various face identities. However, this challenge has not been met largely due to the lack of simultaneous access to the activity of the entire face processing neural network as well as the lack of a comprehensive multifaceted model that is able to characterize a large number of facial features. In this study, we address this challenge by conducting in silico experiments using a deep neural network (DNN) capable of face recognition with a diverse array of stimuli. We identified a subset of DNN neurons selective to face identities, and these identity-selective neurons demonstrated generalized discriminability to novel faces not involved in the training and in many different styles. Visualization of the network explained the response of the DNN neurons and manipulation of the network confirmed the importance of identity-selective neurons in face recognition. Importantly, using our human single-neuron recordings, we directly compared the response of artificial neurons with 490 real human neurons to the same stimuli and found that artificial neurons did share a similar representation of facial features as human neurons. We also observed a novel region-based feature coding mechanism in DNN neurons as in human neurons, which may explain how the DNN performs face recognition. Together, by directly linking between artificial and human neurons, our results shed light on how human neurons encode face identities.

scholarly journals Research on Face Recognition Algorithm Based on Improved Residual Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 46
Author(s):  
Tang Xiaolin ◽  
Wang Xiaogang ◽  
Hou Jin ◽  
Han Yiting ◽  
Huang Ye
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition Algorithm

scholarly journals Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ahmed Jawad A. AlBdairi ◽  
Zhu Xiao ◽  
Mohammed Alghaili
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Research Community ◽  
Facial Features ◽  
New Model ◽  
Image Dataset ◽  
Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

Self- or familiar-face recognition advantage? New insight using ambient images

2018 ◽  
Vol 71 (6) ◽  
pp. 1396-1404 ◽  
Author(s):  
Catherine Bortolon ◽  
Siméon Lorieux ◽  
Stéphane Raffard
Keyword(s):  
Face Recognition ◽  
Graduate Students ◽  
Matching Task ◽  
Healthy Individuals ◽  
Familiarity Effect ◽  
Familiar Face ◽  
The Past ◽  
The Face ◽  
First Time ◽  
Unfamiliar Faces

Self-face recognition has been widely explored in the past few years. Nevertheless, the current literature relies on the use of standardized photographs which do not represent daily-life face recognition. Therefore, we aim for the first time to evaluate self-face processing in healthy individuals using natural/ambient images which contain variations in the environment and in the face itself. In total, 40 undergraduate and graduate students performed a forced delayed-matching task, including images of one’s own face, friend, famous and unknown individuals. For both reaction time and accuracy, results showed that participants were faster and more accurate when matching different images of their own face compared to both famous and unfamiliar faces. Nevertheless, no significant differences were found between self-face and friend-face and between friend-face and famous-face. They were also faster and more accurate when matching friend and famous faces compared to unfamiliar faces. Our results suggest that faster and more accurate responses to self-face might be better explained by a familiarity effect – that is, (1) the result of frequent exposition to one’s own image through mirror and photos, (2) a more robust mental representation of one’s own face and (3) strong face recognition units as for other familiar faces.

scholarly journals Familiar and unfamiliar face recognition in crested macaques ( Macaca nigra )

2015 ◽  
Vol 2 (5) ◽  
pp. 150109 ◽  
Author(s):  
Jérôme Micheletta ◽  
Jamie Whitehouse ◽  
Lisa A. Parr ◽  
Paul Marshman ◽  
Antje Engelhardt ◽  
...  
Keyword(s):  
Face Recognition ◽  
Social Factors ◽  
Novel Species ◽  
Primate Species ◽  
Pictorial Representation ◽  
Facial Features ◽  
Dominance Hierarchies ◽  
Macaca Nigra ◽  
Unfamiliar Face ◽  
Unfamiliar Faces

Many species use facial features to identify conspecifics, which is necessary to navigate a complex social environment. The fundamental mechanisms underlying face processing are starting to be well understood in a variety of primate species. However, most studies focus on a limited subset of species tested with unfamiliar faces. As well as limiting our understanding of how widely distributed across species these skills are, this also limits our understanding of how primates process faces of individuals they know, and whether social factors (e.g. dominance and social bonds) influence how readily they recognize others. In this study, socially housed crested macaques voluntarily participated in a series of computerized matching-to-sample tasks investigating their ability to discriminate (i) unfamiliar individuals and (ii) members of their own social group. The macaques performed above chance on all tasks. Familiar faces were not easier to discriminate than unfamiliar faces. However, the subjects were better at discriminating higher ranking familiar individuals, but not unfamiliar ones. This suggests that our subjects applied their knowledge of their dominance hierarchies to the pictorial representation of their group mates. Faces of high-ranking individuals garner more social attention, and therefore might be more deeply encoded than other individuals. Our results extend the study of face recognition to a novel species, and consequently provide valuable data for future comparative studies.

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