InnovFaceNet: Deep Face Recognition for Industrial Environments

In recent times the usage of intelligent systems have paved way formany applications to be robust and self-reliant. One such popularand vast growing technology is face recognition. Facial Recognitiontechnology is used in security, surveillance, criminal justice systemsand many other multimedia platforms. This work proposes a realtime facial recognition technology which can be used in any industrialsetup eliminating manual supervision, ensuring authorized accessto the personnel in the plant. Due to the recent development ofCOVID-19 pandemic around the world, wearing masks has becomea necessity. Our proposed facial recognition technology identifies aperson’s face with mask or no mask in real time with a speed of20 FPS on a CPU and an F1-score of 95.07%. This makes ouralgorithm fast, secure, robust and deployable on a simple personalcomputer or any edge device at any industrial plant or organization.

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Real-Time Gaze-Aware Cognitive Support System for Security Surveillance

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641274 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1145-1149

Author(s):

Alexandre Marois ◽

Daniel Lafond ◽

Alexandre Williot ◽

François Vachon ◽

Sébastien Tremblay

Keyword(s):

Visual Search ◽

Real Time ◽

Support System ◽

Intelligent Systems ◽

Incident Detection ◽

Task Interruption ◽

Scanning Behavior ◽

Security Surveillance ◽

The Impact ◽

Scan Pattern

Security surveillance entails many cognitive challenges (e.g., task interruption, vigilance decrements, cognitive overload). To help surveillance operators overcome these difficulties and perform more efficient visual search, gaze-based intelligent systems can be developed. The present study aimed at testing the impact of the Scantracker system—which pinpointed neglected cameras while detecting and correcting attentional tunneling and vigilance decrease—on human scanning behavior and surveillance performance. Participants took part in a surveillance simulation, monitoring cameras and searching for ongoing incidents, and half of them was supported by the Scantracker. Although behavioral surveillance performance was not improved, participants supported by the Scantracker showed more efficient gaze-based measures of surveillance. Moreover, some of these measures were associated with performance, suggesting that scan pattern improvements might lead indirectly to more efficient incident detection. Overall, these results speak to the potential of using gaze- aware intelligent systems to support surveillance operators.

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Implementation of Aadhaar Verified, face Recognition based Security Surveillance

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7183.079920 ◽

2020 ◽

Vol 9 (9) ◽

pp. 415-420

Keyword(s):

Face Recognition ◽

Real Time ◽

Human Face ◽

Element Determination ◽

Python Language ◽

Cascade Calculation ◽

Haar Cascade ◽

Two Phases ◽

Security Surveillance ◽

Human Faces

Security and Authentication is a basic piece of any industry. In Real time, Human face acknowledgment can be acted in two phases, for example, Face discovery and Face acknowledgment. This paper actualizes "Haar-Cascade calculation" to distinguish human faces which are sorted out in Open CV by Python language. Gathering with other existing calculations, this classifier creates a high acknowledgment rate even with shifting articulations, effective element determination and low combination of bogus positive highlights. Haar highlight based course classifier framework uses just 200 highlights out of 6000 highlights to yield an acknowledgment pace of 85-95%.

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REAL- TIME ATTENDANCE SYSTEM USING FACE RECOGNITION TECHNIQUE

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i09.043 ◽

2021 ◽

Vol 5 (9) ◽

Author(s):

Debadrita Ghosh

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Face Recognition ◽

Real Time ◽

Machine Learning Algorithms ◽

Huge Number ◽

Internet Connection ◽

The World ◽

Almost All ◽

Recognition Technique

With increasing technologies and scientific knowledge, today’s world has resulted in a great change in almost all aspects. Technical facilities, machine learning, algorithms and other aspects are playing a huge role in almost every part of the world. Taking this into consideration, this research was developed by us, which includes face recognition, face detection and feature extraction. This research is based on real time face recognition for attendance, as it may help in huge number of institutions and other sectors. Here no physical involvement of invigilator is required. The system will totally conduct the processes based on better internet connection and better illumination. An extra feature has been added which includes the details of the pupil to be emailed to their guardian. It’s undertaking is done with Python 3.7.6, OpenCV 3.4. and Anaconda Navigator(Anaconda3). The proposed arrangement is tried for different light intensities and conditions.

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Public attitudes towards the use of automatic facial recognition technology in criminal justice systems around the world

PLoS ONE ◽

10.1371/journal.pone.0258241 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0258241

Author(s):

Kay L. Ritchie ◽

Charlotte Cartledge ◽

Bethany Growns ◽

An Yan ◽

Yuqing Wang ◽

...

Keyword(s):

Criminal Justice ◽

Facial Recognition ◽

Public Attitudes ◽

International Survey ◽

Private Companies ◽

Criminal Justice Systems ◽

The World ◽

The Usa ◽

Justice Systems ◽

The Uk

Automatic facial recognition technology (AFR) is increasingly used in criminal justice systems around the world, yet to date there has not been an international survey of public attitudes toward its use. In Study 1, we ran focus groups in the UK, Australia and China (countries at different stages of adopting AFR) and in Study 2 we collected data from over 3,000 participants in the UK, Australia and the USA using a questionnaire investigating attitudes towards AFR use in criminal justice systems. Our results showed that although overall participants were aligned in their attitudes and reasoning behind them, there were some key differences across countries. People in the USA were more accepting of tracking citizens, more accepting of private companies’ use of AFR, and less trusting of the police using AFR than people in the UK and Australia. Our results showed that support for the use of AFR depends greatly on what the technology is used for and who it is used by. We recommend vendors and users do more to explain AFR use, including details around accuracy and data protection. We also recommend that governments should set legal boundaries around the use of AFR in investigative and criminal justice settings.

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Face Recognition Door Unlock System

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1229.09811s19 ◽

2019 ◽

Vol 8 (11S) ◽

pp. 1133-1139

Keyword(s):

Face Recognition ◽

Real Time ◽

Facial Recognition ◽

First Grade ◽

Recognition System ◽

Video Frame ◽

Raspberry Pi ◽

Face Recognition System ◽

Essential Components ◽

The Face

Face recognition system is widely used for human identification particularly for security functions. The project deals with the look and implementation of secure automatic door unlockby using Raspberry Pi. Web camera for capturing the images from the video frame is operated and controlled by raspberry pi using Open CVPython library to train and store human faces for recognition. In this project we are using Raspberry Pi as face recognition module to capture human images and it will compare with stored data base images. If it matches with authorized user then system allows to supply power to electromagnetic lock to create magnetic field for unlocking the door. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts all unauthorized users from highly secured areas and aids in minimizing human error. Face recognition is one of the most Secured System than the biometric pattern recognition technique which is used in a large spectrum of applications.The time and accuracy factor is considered about the major problem which specifies the performance of automatic face recognition in real time environments. Various solutions have been proposed using multicore systems. By considering present challenge, this provides the complete architectural design and proposes an analysis for a real time face recognition. Thus, the image extracted and allowed to match with the database pictures. If the images are matched, the door unlocks mechanically. the planning of the face recognition system exploitation Raspberry pi will create the smaller, lighter and with lower power consumption, therefore it's a lot of convenient than the PC-based face recognition system. Principle element analysis LBPH (Local Binary Pattern Histogram) algorithmic program is employed for the face recognition and detection method. Then acknowledgement are send through Zigbee module from transmitter to receiver. If image isn't detected in database then it'll ask for manual four digit pin for unlocking the door.The developed theme is affordable, fast, and extremely reliable and provides enough flexibility to suits any environment of various systems. Problem Statement:In theworld of emerging technology, security became an essential component in day to day life. Information theft, lack of security and violation of privacy etc. are the essential components which are needed to be protected. Using smart secure systems for door lock and unlocking became popular nowadays. This is system is being adapted by many countries and first grade countries such as USA, Japan etc., already makes use of this system. This system provides either a facial recognition security feature or a keypad is provided to enter the passcode which unlocks the door. Although, it provides security to the doors, it also has somelimitations and drawbacks: Firstly, if the system mainly uses a facial recognition module, there might be a slight chance that sometimes the face may not be detected and hence the door cannot be unlocked. Secondly, if the system uses a

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Perceptual Effects and Recollective Experience in Face Recognition

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169.52.3.224 ◽

2005 ◽

Vol 52 (3) ◽

pp. 224-231 ◽

Cited By ~ 7

Author(s):

Chrisanthi Nega

Keyword(s):

Face Recognition ◽

Recognition Memory ◽

Congruency Effect ◽

Facial Recognition ◽

Memory Systems ◽

Study Time ◽

Perceptual Fluency ◽

Recollective Experience ◽

Trace Strength ◽

Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

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Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0220 ◽

2017 ◽

Vol 7 (7) ◽

pp. 302

Author(s):

Reshma P ◽

Muneer VK ◽

Muhammed Ilyas P

Keyword(s):

Face Recognition ◽

Real Time ◽

High Accuracy ◽

Efficient Technique ◽

Human Face ◽

Traditional Methods ◽

Person Recognition ◽

The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

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Real Time Gender and Age Prediction using Deep Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2906.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 797-801

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Real Time ◽

Vital Role ◽

Learning Techniques ◽

Gender And Age ◽

The Face ◽

And Gender ◽

Gender Detection ◽

Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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