Identity Verification Using Face Recognition for Artificial-Intelligence Electronic Forms with Speech Interaction

2019 ◽  
pp. 52-66 ◽  
Author(s):  
Akitoshi Okumura ◽  
Shuji Komeiji ◽  
Motohiko Sakaguchi ◽  
Masahiro Tabuchi ◽  
Hiroaki Hattori
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Identity Verification ◽  
Speech Interaction

Application of Artificial Intelligence in Adaptive Face Recognition System

2021 ◽  
Author(s):  
Rhose Anne C. Garcia ◽  
Raymond Paolo Lacayanga ◽  
Febus Reidj G. Cruz
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

scholarly journals Computational insights into human perceptual expertise for familiar and unfamiliar face recognition

2019 ◽  
Author(s):  
Nicholas Blauch ◽  
Marlene Behrmann ◽  
David C. Plaut
Keyword(s):  
Face Recognition ◽  
Theoretical Work ◽  
Fine Tuning ◽  
Identity Verification ◽  
High Performing ◽  
Face Identity ◽  
Unfamiliar Face ◽  
Perceptual Representations ◽  
Expert Networks ◽  
Identity Perception

Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.

scholarly journals Dynamic & Intelligent Student Attendance Maintenance Based on Face Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 399-403
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Face Detection ◽  
Student Attendance ◽  
Teachers And Students ◽  
The Face ◽  
Monitoring Camera ◽  
On Line

The proposed system generally results a solution to some of the problems which occurs in colleges and schools by providing a monitoring camera with the help of “Artificial Intelligence (AI)” . The main problem which can be occurred is wastage of time in taking the attendance manually or through any biometric sensors. The next problem which can be solved is to control the usage of electricity in classrooms when students are not in class. When the videos are getting recorded with the help of monitoring cameras, at the same time the head counting and face detection of the students present will also be done. When the strength of the class is zero ,the head counting also results to zero. The electricity can also be saved at the same time when people are not present in the classroom. The face recognition is the easiest process which can be done for marking the attendance, where the attendance is marked automatically. This process also helps to prevent the fake attendance. Face recognition and detection is generally based on line edge mapping to attain the identity of the student and also meets the wants of attendance in the universities and schools. The image of the student is to be captured and checked with the database simultaneously and marks the attendance of the particular student. The video gets recorded all the time and checks whether the student remains in class for the entire period.The attendance marking system with the help of technology is very essential for both the teachers and students.

Research on Face Recognition Sports Intelligence Training Platform Based on Artificial Intelligence

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
Jie Yang ◽  
Lian Tang ◽  
Xin-Wei Li
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Face Recognition ◽  
Control Unit ◽  
Training System ◽  
Table Tennis ◽  
Training Problem ◽  
Tennis Players ◽  
Industrial Control Computer ◽  
Human Behavior Recognition

With the application of artificial intelligence in many social fields, the research of human behavior recognition and non-contact detection of human physiological parameters based on face recognition and other technologies has developed rapidly, and the application of artificial intelligence in culture, sports and entertainment has also begun to rise. How to apply the existing mature technology to the sports intelligence training system taking table tennis as an example is a hot issue worthy of study. In this paper, a comprehensive intelligent table tennis training system and platform based on Convolutional Neural Network face recognition and face heart rate detection is designed, which is mainly used to solve the philosophical training problem in table tennis. In the system place, an identification cameras is set at the entrance of table tennis training places, which is used for table tennis players’ sign-in and training table number allocation, and an intelligent analysis cameras is set above each intelligent training table, which is used for detecting the face and heart rate of table tennis players. Each intelligent training platform consists of intelligent voice control unit, server, camera, industrial control computer, monitor and other terminal modules. The member data center constitutes the platform of intelligent table tennis training system.

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 Identity Verification of Ticket Holders at Large-scale Events Using Face Recognition

2017 ◽  
Vol 25 (0) ◽  
pp. 448-458 ◽  
Author(s):  
Akitoshi Okumura ◽  
Takamichi Hoshino ◽  
Susumu Handa ◽  
Yugo Nishiyama ◽  
Masahiro Tabuchi
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Identity Verification
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