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 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 Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

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

Face Recognition Based on Artificial Neural Network: A Review

2021 ◽  
pp. 116-131
Author(s):  
Ameera Alblushi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
Neural Network Approach ◽  
Pattern Recognition Systems ◽  
The Face ◽  
Artificial Neural ◽  
Facial Recognition System

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

scholarly journals A Dual Security Scheme Based on DWT for Personnel Authentication

2019 ◽  
Vol 9 (1) ◽  
pp. 598-602
Keyword(s):  
Rejection Rate ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Discrete Wavelet ◽  
Number Systems ◽  
Biometric Systems ◽  
The Face ◽  
Authentication System ◽  
Facial Recognition System

A biometric identification system that audits the presence of a person using real or behavioral features is safer than passwords and number systems. Present applications are mostly recognize an individual using the single modal biometric system. However, a single characteristic sometimes fails to authenticate accurately. Multimodal biometric technologies solve the problems that exist in the single biometric systems. It is very hard to identify images with low lighting environments using facial recognition system. By utilizing fingerprint recognition, this issue can be better addressed. This paper presents a dual personnel authentication system that incorporates face and fingerprint to improve security. For face identification, the Discrete Wavelet Transform (DWT) algorithm is used to acquire features from the face and fingerprint pictures. The technique used to integrate fingerprint and face is decision level fusion. By adding fingerprint recognition to the scheme, the proposed algorithm decreases the false rejection rate (FRR) in the face and fingerprint recognition and hence increases the accuracy of the authentication.

scholarly journals Design of Facial Recognition System Based on Visual Communication Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xuhui Fu
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Visual Communication ◽  
Facial Recognition ◽  
Recognition System ◽  
Image Capture ◽  
Hardware System ◽  
Face Recognition System ◽  
The Face ◽  
Facial Recognition System

At present, facial recognition technology is a very cutting-edge science and technology, and it has now become a very hot research branch. In this research, first, the thesis first summarized the research status of facial recognition technology and related technologies based on visual communication and then used the OpenCV open source vision library based on the design of the system architecture and the installed system hardware conditions. The face detection program and the image matching program are realized, and the complete face recognition system based on OpenCV is realized. The experimental results show that the hardware system built by the software can realize the image capture and online recognition. The applied objects are testers. In general, the OpenCV-based face recognition system for testers can reliably, stably, and quickly realize face detection and recognition in this situation. Facial recognition works well.

scholarly journals An Separation of Different Facial Expression Recognition System

2019 ◽  
Vol 8 (6S4) ◽  
pp. 438-441
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Digital Data ◽  
Expression Recognition ◽  
Share Data ◽  
The Face ◽  
Facial Recognition System

Face recognition is one of the hot topics in the current world and one of the popular topics of computer studies. Today face recognition in the network society and access to digital data is gaining more attention. The facial recognition system technology is a biometric assessment of a human's face. There are many facial recognition techniques that are intended depending on facial expressions extraction, one of which is 3D facial recognition, as well as their fusion,is difficult. During preprocessing measures for picture recognition to remove only expression-specific characteristics from the face and prevent their issues with a convolution neural network. We can also use some theorems such as LBP and Taylor's theorem to model face recognition. In particular, for cloud robots, we can also use this facial recognition on robots. The robot can perform functions and share data between servers and devices. Seven fundamental expressions are used to identify and classify: happiness, shock, fear, disgust, sadness, rage, and a neutral condition. Until now, the recognition rate is quite up to the expectation stage, but it still tries to enhance. To enhance the recognition frequency of facial image recognition, feelings are chosen by the vibrant Bayesian network technique to depict the development of facial awareness in addition to various emotional operations of facial expressions. The ICCA techniques involve various multivariate sets of distinct facial features that could be eyes, nose, and mouth.

scholarly journals INTEGRATION OF USER INTERFACE WITH FACIAL RECOGNITION SYSTEM

2020 ◽  
pp. 111-115
Author(s):  
D.Manasa ◽  
N.Ramya Sri ◽  
Sk.Naveed ◽  
N.Ramya
Keyword(s):  
Face Recognition ◽  
High Probability ◽  
Facial Recognition ◽  
Recognition System ◽  
Probability Of Error ◽  
Traditional System ◽  
The Face ◽  
Facial Recognition System ◽  
Python Programming ◽  
Recognition Technique

Attendance of students in a large classroom is hard to be handled by the traditional system, as it is time-consuming and has a high probability of error during the process of inputting data into the computer. This paper proposed automated attendance marking system using face recognition technique. The system will help to find the positive and negative of the face and Eigen face algorithm for face recognition by using python programming and OpenCV library. The proposed method using PCA to resolve the problems such as lightning of the images, and the direction of the student faces. The attendance of the student was updated to the Excel sheet after student's face has been recognized. KEYWORDS: PCA, Facial Recognition, ERP, Classroom, Attendance

scholarly journals Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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