A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

scholarly journals APLIKASI PENGENALAN IRIS MATA MENGGUNAKAN METODE HOUGH TRANSFORM DAN GABOR WAVELET

2021 ◽  
Vol 9 (02) ◽  
pp. 105-109
Author(s):  
Fransisca Joanet Pontoh ◽  
Fransiscus Xaverius Senduk ◽  
Inggrit E. G. Pondaag
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
The Other ◽  
Gabor Wavelet ◽  
Identification System ◽  
Biometric System ◽  
The Face ◽  
Iris Patterns ◽  
Identification Techniques ◽  
Unique Part

Biometric system is a development of the basic method of identification system by using the characteristics of humans as it’s object. These include face, fingerprints, signature, palms, iris, ears, sounds even DNA. Face recognition is one of the identification techniques in biometrics that uses part of the face as its parameter. One of the biometric parts of face is Iris. Iris is a unique part of the eyes, this is because the pattern of the somebody eyes will be quite different from the other, even genetically identical twins have different iris patterns. This research will use the Hough and Gabor method to perform iris recognition. The  results show that the application has succeeded in recognizing the selected eye image if the eye image is registered in the database.

The Face Revisited: Using Deleuze and Guattari to Explore the Politics of Algorithmic Face Recognition

2019 ◽  
Vol 37 (1) ◽  
pp. 73-91
Author(s):  
Claudio Celis Bueno
Keyword(s):  
Face Recognition ◽  
Gilles Deleuze ◽  
The Political ◽  
Deleuze And Guattari ◽  
Political Dimension ◽  
The Face ◽  
The Individual ◽  
Clear Relation

This article explores the political dimension of algorithmic face recognition through the prism of Gilles Deleuze and Félix Guattari’s notion of faciality. It argues that algorithmic face recognition is a technology that expresses a key aspect of contemporary capitalism: the problematic position of the individual in light of new forms of algorithmic and statistical regimes of power. While there is a clear relation between modern disciplinary mechanisms of individualization and the face as a sign of individuality, in control societies this relation appears more as a contradiction. The article contends that Deleuze and Guattari’s concepts of machinic enslavement and social subjection offer a fruitful perspective from where to identify the power mechanisms behind the problematic position of the individual in the specific case of algorithmic face recognition.

scholarly journals ILLUMINATION PROCESSING IN FACE RECOGNITION

2014 ◽  
Vol 28 (05) ◽  
pp. 1456011 ◽  
Author(s):  
ZHENXUE CHEN ◽  
CHENGYUN LIU ◽  
FALIANG CHANG ◽  
XUZHEN HAN ◽  
KAIFANG WANG
Keyword(s):  
Adverse Effect ◽  
Face Recognition ◽  
Light Intensity ◽  
Negative Impact ◽  
Processing Method ◽  
Dark Region ◽  
Light Region ◽  
The Face ◽  
Recognition Systems ◽  
Better Than

Changes in light intensity and angle present a major challenge to the creation of reliable face recognition systems. The existence of bright regions and dark regions has been shown to have a serious negative impact on the performance of face recognition systems. This paper proposes a solution to this problem based on self-quotient image (SQI) processing method. In this method, bright and dark areas are processed separately without changing the essential characteristics of the image of the face. The dark and light areas are processed separately by SQI. Experimental results indicate that this Single-Light-Region and Single-Dark-Region SQI method removes the adverse effect of multi-bright and multi-dark areas better than competing methods.

scholarly journals Who_Is : Identification system of human faces

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Mohamed Tayeb Laskri ◽  
Djallel Chefrour
Keyword(s):  
Face Recognition ◽  
Geometric Model ◽  
Personal Identification ◽  
Identification System ◽  
K Nearest Neighbors ◽  
City Block ◽  
The Face ◽  
Characteristic Points ◽  
The Individual ◽  
Application Fields

International audience Although human face recognition is a hard topic due to many parameters involved (e.g. variability of the position, lighting, hairstyle, existence of glasses, beard, moustaches, wrinkles...), it becomes of increasing interest in numerous application fields (personal identification, video watch, man machine interfaces...). In this work, we present WHO_IS, a system for person identification based on face recognition. A geometric model of the face is definedfrom a set of characteristic points which are extracted from the face image. The identification consists in calculating the K nearest neighbors of the individual test by using the City-Block distance. The system is tested on a sample of 100 people with a success rate of 86 %. Bien que la reconnaissance des visages humains soit un domaine difficile à cause de la multitude des paramètres qu'il faut prendre en compte (variation de posture, éclairage, style de coiffure, port de lunettes, de barbes, de moustaches, vieillesse…), il est très important de s'en intéresser vu les nombreux champs d'applications (vérification de personnes, télésurveillance, interfaces homme-machine …). Dans ce travail nous présentons la mise en œuvre de WHO_IS, un système d'identification de personnes par reconnaissance des visages humains. Nous avons développé un modèle géométrique du visage basé sur un ensemble de points caractéristiques extraits à partir de l'image du visage. La procédure d'identification consiste à calculer les K plus proches voisins de l'individu test dans le sens de la distance City-Block. Le système WHO_IS a été testé sur un échantillon de 100 personnes. Un taux de reconnaissance correcte de 86% a été obtenu

scholarly journals Automatic Attendance System Using Face Recognition Technique

2020 ◽  
Vol 9 (1) ◽  
pp. 2134-2138
Keyword(s):  
Face Recognition ◽  
User Interface ◽  
Graphical User Interface ◽  
Cascade Algorithm ◽  
The Face ◽  
Haar Cascade ◽  
Recognition Technique

Attendance system is very important in schools and colleges’ Manual attendance system has many difficulties like it may less accurate and critical to maintain. So, attendance system using face recognition technique increase the accuracy and also it required less time than other methods. There are many existing system for attendance such as face recognition using IoT, PIR sensors and so on. For face recognition, hardware devices also helpful. But challenge is that to maintain all the sensors properly without get damage. After studying all method and techniques we are trying to implement a system with Haar Cascade Algorithm which has highest accuracy among all. It is able to capture the images from 50-70cm. We are creating graphical user interface which capture the images, create the dataset and train the dataset on single click. After recognizing the face it will display name of student and roll number. That information stored in attendance sheet automatically with time and date.

scholarly journals Face Detection and Gender Classification using Facial Features

2019 ◽  
Vol 8 (4) ◽  
pp. 6670-6674
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Facial Features ◽  
Gender Classification ◽  
Biometric System ◽  
Detection Techniques ◽  
The Face ◽  
And Gender ◽  
Two Stages

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

The Application of Biometric Identification Technology in ATM System

2013 ◽  
Vol 347-350 ◽  
pp. 3419-3421
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Second Generation ◽  
Fingerprint Recognition ◽  
Biometric Identification ◽  
Automatic Identification ◽  
Behavioral Characteristics ◽  
Automatic Teller Machine ◽  
Biological Recognition ◽  
The Face ◽  
Bank System

Biometric identification technology deals with the identification of individuals based on their biological or human behavioral characteristics. Biometric identification method is reliable, anti-counterfeit, convenient and safe. At present there are some insecurity factors in the ATM (automatic teller machine) in bank system. Methods such as combining biological recognition with the ATM machine, adding the face recognition technology, fingerprint recognition, second generation ID card recognition, enhancing automatic identification are developed to improve the security of ATM.

scholarly journals Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

2020 ◽  
pp. 34-44
Author(s):  
Md. Mahbubul Alam ◽  
Md. Ashikur Rahman Khan ◽  
Zayed Us Salehin ◽  
Main Uddin ◽  
Sultana Jahan Soheli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Biometric System ◽  
Recognition Method ◽  
Person Identification ◽  
Multimodal Biometric System ◽  
Recognition Efficiency

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

scholarly journals Age-based facial recognition using convoluted neural network deep learning algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 424
Author(s):  
Julius Yong Wu Jien ◽  
Aslina Baharum ◽  
Shaliza Hayati A. Wahab ◽  
Nordin Saad ◽  
Muhammad Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Well Being ◽  
Deep Learning Algorithm ◽  
Business Applications ◽  
Image Dataset ◽  
The Face ◽  
The Individual

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

scholarly journals Face Recognition Using the Convolutional Neural Network for Barrier Gate System

2021 ◽  
Vol 15 (10) ◽  
pp. 138
Author(s):  
Mochammad Langgeng Prasetyo ◽  
Achmad Teguh Wibowo ◽  
Mujib Ridwan ◽  
Mohammad Khusnu Milad ◽  
Sirajul Arifin ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Recognition System ◽  
Average Error ◽  
The Real ◽  
The Face ◽  
Gate System ◽  
Recognition Technique

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

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