scholarly journals Implementation of Aadhaar Verified, face Recognition based Security Surveillance

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%.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

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.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

scholarly journals InnovFaceNet: Deep Face Recognition for Industrial Environments

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Nagarjun Gururaj ◽  
Kanika Batra
Keyword(s):  
Face Recognition ◽  
Criminal Justice ◽  
Real Time ◽  
Intelligent Systems ◽  
Facial Recognition ◽  
Industrial Plant ◽  
The World ◽  
Industrial Environments ◽  
Or Organization ◽  
Security Surveillance

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.

A Real-time Face Recognition Smart Attendance System with Haar Cascade Classifiers

2021 ◽  
Author(s):  
Rupak Bairagi ◽  
Remon Ahmed ◽  
Sadia Afrin Tisha ◽  
Md. Sumon Sarder ◽  
Md. Sabiqul Islam ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Cascade Classifiers ◽  
Haar Cascade ◽  
Haar Cascade Classifiers

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.

3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

Human Face Recognition Applying Haar Cascade Classifier

2022 ◽  
pp. 143-157
Author(s):  
F. M. Javed Mehedi Shamrat ◽  
Anup Majumder ◽  
Probal Roy Antu ◽  
Saykot Kumar Barmon ◽  
Itisha Nowrin ◽  
...  
Keyword(s):  
Face Recognition ◽  
Human Face ◽  
Cascade Classifier ◽  
Human Face Recognition ◽  
Haar Cascade

scholarly journals Feature Based Face Recognition using Machine Learning Technniques

2020 ◽  
Vol 8 (6) ◽  
pp. 1313-1317
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Recognition ◽  
Euclidean Distance ◽  
Human Face ◽  
Cosine Transformation ◽  
The Face ◽  
Feature Based ◽  
Haar Cascade ◽  
Distinct Features

Human Face has Numerous unique Features to Distinguish between each other. Face can Identified by distinguishing between face and non-face followed by Identification. Traditionally face recognition uses distinct features Comparison to Identify the Faces which is Complex for larger databases and ambiguous in many scenarios. To improve the accuracy and Scalability Proposed method uses machine learning based Haar Cascade technique for face detection and convolutional neural network is used for feature extraction followed by classification using Euclidean distance and cosine transformation to recognize the face. The results demonstrate the work is performed well in recognizing the face efficiently with different variations.

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

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