scholarly journals Implementation of security system for bank using open CV and RFID

2018 ◽  
Vol 7 (2.7) ◽  
pp. 187 ◽  
Author(s):  
Kona Neeraja ◽  
Potu Rama Chandra Rao ◽  
Dr Suman Maloji ◽  
Dr Mohammed Ali Hussain
Keyword(s):  
Face Recognition ◽  
Banking Sector ◽  
Security Policies ◽  
Human Face ◽  
Cascade Classifier ◽  
Biometric System ◽  
Time Period ◽  
Proposed Model ◽  
Locker Room ◽  
Haar Cascade

Security is one of the major concerns in banking sector where we can adapt the latest techniques like face recognition and RFID we can provide better security policies. In this paper we have proposed a model with two major security techniques. The First method provides security for the locker room door by face recognition using OpenCV. Face recognition is a particular type of biometric system that can be used to analyze the obtained information and identify the user uniquely by the trained images. In this proposed model the images of customers are trained. A Microsoft Lifecam HD-3000 is placed outside the locker room. This camera detects the human face using Haar Cascade Classifier and recognizes a customer using LBPH Algorithm. If a trained customer tries to enter then door is unlocked. The customer name is uploaded to cloud. The second method provides security to the cashier cabin by using MFRC522 RFID Module which is very easy to access which consumes less time and more secured compared to the existing system. When an authorized tag is recognized the door is unlocked for certain time period and the userid is uploaded to the cloud. By using these two techniques we can provide security for locker room and cashier cabins in any banking sector.

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 Liveness Detection Based on Human eye Blinking for Photo Attacks

2019 ◽  
Vol 9 (1) ◽  
pp. 4074-4077
Keyword(s):  
Face Recognition ◽  
Aspect Ratio ◽  
Face Verification ◽  
Human Face ◽  
Eye Blink ◽  
Liveness Detection ◽  
Small Window ◽  
Time Period ◽  
Accuracy Result ◽  
Security Check

Auto face recognition mainly implemented to avoid the replication of identity to demonstrate through security check. This rage of face verification has brought intensive interest about facial biometric towards attacks of spoofing, in which a person’s mask or photo can be produced to be authorized. So, we propose a liveness detection based on eye blinking, where eyes are extracted from human face. The method of face recognition was applied by utilizing OpenCV classifier and dlib library, and a concept of edge detection and calculation of structure to extract the portion of the eye and to observe and make note of variation in the attributes of the eyes over a time period was employed. The landmarks are plotted accurately enough to derive the state of eye if it is closed or opened. A scalar quantity EAR (eye aspect ratio) is derived from landmark positions defined by the algorithm to identify a blink corresponding to every frame. The set of EAR values of successive frames are detected as a eye blink by a OpenCV classifier displayed on a small window when person is in front of camera. Finally, it gives the accuracy result whether it is human being or spoof attack.

scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

scholarly journals Visitor/Intruder Monitoring System using Haar-cascade Classifier Algorithm

2020 ◽  
pp. 229-231
Author(s):  
Jenifa G ◽  
Yuvaraj N ◽  
SriPreethaa K R
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Monitoring System ◽  
Face Detection ◽  
Security System ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
Cascade Classifier ◽  
Home Security ◽  
Haar Cascade

Home security system plays a predominant role in the modern era. The purpose of the security systems is to protect the members of the family from intruders. The main idea behind this system is to provide security for residential areas. In today’s world securing our home takes a major role in the society. Surveillance from home to huge industries, plays a significant role in the fulfilment of our security. There are many machine learning algorithms for home security system but Haar-cascade classifier algorithm gives a better result when compared with other machine learning algorithm This system implements a face recognition and face detection using Haar-cascade classifier algorithm, OpenCV libraries are used for training and testing of the face detection process. In future, face recognition will be everywhere in the world. Face recognition is creating a magic in every field with its advanced technology. Visitor/Intruder monitoring system using Machine Learning is used to monitor the person and find whether the person is a known or unknown person from the captured picture. Here LBPH (Local Binary Pattern Histogram) Face Recognizer is used. After capturing the image, it is compared with the available dataset then their respective name and picture is sent to the specified email to alert the owner.

scholarly journals An Improved Local Binary Patterns Histograms Technique for Face Recognition for Real Time Applications

2019 ◽  
Vol 8 (2S7) ◽  
pp. 524-529 ◽  
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Facial Features ◽  
Cascade Classifier ◽  
The Past ◽  
Face Recognition System ◽  
Unique System ◽  
Haar Cascade ◽  
Python Programming

Recently, face recognition and its applications has been considered as one of the image analysis most successful applications, especially over the past several years. Face Recognition is a unique system that can be used by using unique facial features for identification or verification of a person from a digital image. In a face recognition system, there are many technique that can be used. This paper provides an efficient of the Local Binary Patterns Histograms (LBPH) based technique provided by OpenCV library which is implemented in Python programming language which is well suitable for realistic scenarios. In this paper we also provide visual qualitative outcome with existing algorithm (Haar-cascade classifier and Local Binary Patterns Histograms (LBPH)). As a result, the proposed technique outperform better in terms of visual qualitative analysis.

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.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

scholarly journals Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

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%.

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