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 DATABASE MANAGEMENT FOR FACIAL RECOGNITION SYSTEM

2020 ◽  
pp. 128-131
Author(s):  
Sk. Naveed ◽  
N.Ramya ◽  
D.Manasa ◽  
N. Ramya Sri
Keyword(s):  
Face Recognition ◽  
Personal Characteristics ◽  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Training Images ◽  
The Face ◽  
Facial Recognition System ◽  
The Individual ◽  
And Training

The face is one of the easiest ways to differentiate the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics or facial features of a person to identify the person's identity. The most used human face recognition process is face detection ,where this procedure takes place very quickly in humans, except under certain conditions where the object is located at close distance. The purpose of this project is to develop face recognition based automated student attendance system. In order to achieve high quality performance, the test images and training images of this proposed approach are limited to frontal and upright facial images that consist of a single face only. The test images and training images have to be captured by using the same device to ensure no quality difference. In addition, the students have to register in the database to be recognized. The enrolment can be done on the spot through the user-friendly interface.

scholarly journals FACE RECOGNITION FOR THE AUTOMATIC ATTENDANCE MANAGEMENT SYSTEM

2020 ◽  
pp. 116-120
Author(s):  
N.Ramya Sri ◽  
D.Manasa ◽  
N.Ramya ◽  
Sk.Naveed
Keyword(s):  
Face Recognition ◽  
Real Life ◽  
Personal Characteristics ◽  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Information Process ◽  
White Balance ◽  
Face Recognition System ◽  
The Face

Face Recognition is a currently developing technology with multiple real- life applications. The goal of this Thesis is to develop a complete Face Recognition system. The system uses Convolutional Neural Networks in order to extract relevant facial features. These features allow to compare faces between them in an efficient way. The system can be trained to recognize a set of people, and to learn in an on-line way, by integrating the new people. Face recognition system is one of the biometric information process its applicability is easier and working range is larger than others .The face recognition is live acquired images without any application field in mind .process utilized in the system are White Balance correction ,skin like region segmentation .facial feature extraction and face image extraction on a face Candidate .The face one of the easiest ways to distinguish the individual identify each other .Face recognition is a personal identification system that uses personal characteristics of a person to identify the person’s identify. KEYWORDS:-Face Recognition, Facial Attendance, Automatic Attendance, Face Detection.

scholarly journals System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 578 ◽  
Author(s):  
Moisés Márquez-Olivera ◽  
Antonio-Gustavo Juárez-Gracia ◽  
Viridiana Hernández-Herrera ◽  
Amadeo-José Argüelles-Cruz ◽  
Itzamá López-Yáñez
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Hybrid Model ◽  
Integrated System ◽  
K Nearest Neighbors ◽  
The People ◽  
Proposed Model ◽  
The Face ◽  
Controlled Environments ◽  
The Individual

Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

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 Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

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 Performance Evolution of Face and Speech Recognition system using DTCWT and MFCC Features

2021 ◽  
Vol 12 (3) ◽  
pp. 3395-3404
Author(s):  
Shanthakumar H.C Et.al
Keyword(s):  
Face Recognition ◽  
Speech Recognition ◽  
Recognition System ◽  
Personal Identification ◽  
Personal Identification Number ◽  
Complex Wavelet Transform ◽  
Mel Frequency Cepstral Coefficients ◽  
Performance Variables ◽  
Person Recognition ◽  
The Face

Every activity in day-to-day life is required the need of mechanized automation for ensuring the security. The biometrics security system provides the automatic recognition of human by overcoming the traditional recognition methods like Password, Personal Identification Number and ID cards etc. The face recognition is a wide research with many applications. In the proposed work face recognition is carried out using DTCWT (Dual Tree Complex Wavelet Transform) integrated with predominant QFT (Quick Fourier Transform) and speech recognition is carried out using MFCC (Mel Frequency Cepstral Coefficients) algorithm. The distance formula is used for matching the test features and database features of the face and speech images. Performance variables such as EER, FRR, FAR and TSR are evaluated for person recognition

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.

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