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 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.

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 Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

Biometrical Processing of Faces in Security and Forensics

2010 ◽  
pp. 79-103
Author(s):  
Pawel T. Puslecki
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Face Processing ◽  
Facial Features ◽  
Comprehensive Description ◽  
Facial Processing ◽  
Human Faces

The aim of this chapter is the overall and comprehensive description of the machine face processing issue and presentation of its usefulness in security and forensic applications. The chapter overviews the methods of face processing as the field deriving from various disciplines. After a brief introduction to the field, the conclusions concerning human processing of faces that have been drawn by the psychology researchers and neuroscientists are described. Then the most important tasks related to the computer facial processing are shown: face detection, face recognition and processing of facial features, and the main strategies as well as the methods applied in the related fields are presented. Finally, the applications of digital biometrical processing of human faces are presented.

scholarly journals Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1595
Author(s):  
Seong-Kyu Kim ◽  
Jun-Ho Huh
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Non Fiction ◽  
Legal Problems ◽  
Artificial Neurons ◽  
The Will ◽  
Artificial Neural ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Artificial Network

This paper discusses the worldwide trend of aging as the lifespan of humans increases. Nonetheless, most people do not write wills, which results in many legal problems after their death. There are many reasons for this including the problem of the validity of their heritage possibly not being legally certified. Wills can be divided into two categories, i.e., testimony and documents. A lawyer in the middle should notarize them, however, instead of providing these notarized services, we propose more transparent algorithms, blockchain shading, and smart country functions. Architectures are designed based on a neural network, the blockchain deep neural network (DNN), and deep neural network-based units are built with a necessary artificial neural network (ANN) base. A heritage inherited blockchain architecture is designed to communicate between nodes based on the minimum distance algorithm and multichannel protocol. In addition, neurons refer to the nerve cells that make up the nervous system of an organism, and artificial neurons are an abstraction of the functions of dendrite, soma, and axon that constitute the neurons of an organism. Similar to the neurons in organisms, artificial neural algorithms such as the depth-first search (DFS) algorithm are expressed in pseudocode. In addition, all blockchain nodes are equipped with verified nodes. A research model is proposed for an artificial network blockchain that is needed for this purpose. The experimental environment builds the server and network environments based on deep neural networks that require verification. Weights are also set for the required verification and performance. This paper verifies the blockchain algorithm equipped with this non-fiction preprocessor function. We also study the blockchain neuron engine that can safely construct a block node for a suicide blockchain. After empirical testing of the will system with artificial intelligence and blockchain, the values are close to 2 and 10 and the distribution is good. The blockchain node also tested 50 nodes more than 150 times, and we concluded that it was suitable for actual testing by completing a demonstration test with 4500 TPS.

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%.

Sign in / Sign up

Export Citation Format

Share Document