scholarly journals A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

2022 ◽  
Vol 12 (2) ◽  
pp. 1468
Author(s):  
Sangamesh Hosgurmath ◽  
Viswanatha Vanjre Mallappa ◽  
Nagaraj B. Patil ◽  
Vishwanath Petli
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Distance Ratio ◽  
Research Areas ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

scholarly journals A Face Recognition System using Directional Binary Code Algorithm and Multi-SVM

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1201-1208
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Binary Code ◽  
Recognition System ◽  
Extraction Methods ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Face Recognition System ◽  
The Face

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.

scholarly journals High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Zhou ◽  
Weili Xia
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Real Time ◽  
Task Scheduling ◽  
High Capacity ◽  
Recognition System ◽  
Treatment Area ◽  
Scheduling Model ◽  
Face Recognition System ◽  
The Face

This paper presents an in-depth study of face detection, face feature extraction, and face classification from three important components of a high-capacity face recognition system for the treatment area of hospital and a study of a high-capacity real-time face retrieval and recognition algorithm for the treatment area of hospital based on a task scheduling model. Considering the real-time nature of our system, our face feature extraction network is modeled by DeepID, and the network is slightly improved by introducing a central loss verification signal to train a DeepID-like network model using central loss and use it to extract face features. To further investigate and optimize the schedulability analysis problem of the directed graph real-time task model, this paper proposes a rigorous and approximate response time analysis method for the directed graph real-time task model with an arbitrary time frame. Based on the theoretical results of the greatly additive algebra, it is shown that the coherent qualifying function is linearly periodic, i.e., the function can be represented by a finite nonperiodic part and an infinitely repeated periodic part, thus calculating the coherent qualifying function independent of the magnitude of the interval time. The algorithm for high-capacity real-time face retrieval and recognition in the treatment area of hospital based on the task scheduling model is further investigated, and a face database is established by using the PCA dimensionality reduction technique. Based on the internal architecture of the processor, image preprocessing and IP core packaging are implemented, and the hardware engineering of the high-capacity real-time face recognition system for hospital visits is built using the IP-based design concept. The performance tests of the face detection model and feature extraction network show that the face detection model has a significant reduction in false-positive rate, better fitting of border regression, and improved time performance. The face feature extraction network has no overfitting, and the features are highly discriminative with small feature extraction time consumption. The high-capacity real-time face recognition system for the treatment area of hospital combined with the optimized directed graph task scheduling model can approach 25 fps, which meets the real-time requirements, and the face recognition rate surpasses that of real people. It realizes the intelligence, self-help, and autonomy of medical services and satisfies the medical needs of users in all aspects.

scholarly journals Opening Doors Using Internet Of Things (IoT) Based Face Recognition

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Wahyu Ariansyah ◽  
Dirja Nur Ilham ◽  
Khairuman Khairuman ◽  
Rudi Arif Candra
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Face Recognition ◽  
Internet Of Things ◽  
Digital Image Processing ◽  
Recognition System ◽  
Human Face ◽  
Face Recognition System ◽  
The Face ◽  
Primary Means

Face recognition is a digital image processing approach that uses face photographs as input to identify a person. Face recognition is important since the face is a person's primary means of identification because the shape of a person's face differs significantly, which is easy to do intuitively using the visual senses. Image processing, face detection, feature extraction, and classification are all aspects of the face recognition system, which seeks to determine whether the image obtained is a person's face stored in the database. Principles of operation If a human face appears in front of the camera, the system quickly executes a facial recognition procedure and compares the face to facial data kept on the website. If a face detected by the camera matches the face stored on the website, the solenoid will automatically be in the on position or the door will be open, and vice versa, if the face detected by the camera does not match, the solenoid will remain in the off position or the door will remain locked. This tool can be used to improve the security system on the door of a private room or a room that can only be accessed by certain people.

scholarly journals Feature Extraction Based Face Recognition Using Extreme Learning Machine (ELM)

2016 ◽  
pp. 80-83
Author(s):  
NAGABHAIRAVA VENKATA SIDDARTHA ◽  
MOHAMMAD UMAR ◽  
NABANKUR SEN ◽  
P. KRISHNA PRASAD
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extreme Learning Machine ◽  
Credit Card ◽  
Recognition System ◽  
Extraction Process ◽  
Biometric Identification ◽  
Face Recognition System ◽  
The Face ◽  
Learning Machine

In recent years, Face recognition becomes one of the popular biometric identification systems used in identifying or verifying individuals and matching it against library of known faces. Biometric identification is an actively growing area of research and used in electronic commerce, electronic banking, electronic passports, electronic licences and security applications. Face recognition finds its application in wide variety of areas like criminal identification, human - computer interaction, security systems, credit- card verification, teleconference, image and film processing. This paper suggests an automated face recognition system which extracts the features from the face. Feature extraction process includes locating the position of eyes, nostrils and mouth and determining the distances between those regions. From the extracted features, a database is created for known individuals. A virtual neural network is created based on Extreme Learning Machine (ELM).

scholarly journals Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption

2021 ◽  
Author(s):  
Yatao Yang ◽  
Qilin Zhang ◽  
Wenbin Gao ◽  
Chenghao Fan ◽  
Qinyuan Shu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Privacy Preservation ◽  
Recognition Accuracy ◽  
Homomorphic Encryption ◽  
Recognition System ◽  
Identification Accuracy ◽  
Encryption Algorithm ◽  
Feature Vectors ◽  
Face Recognition System ◽  
The Face

Abstract Face recognition is playing an increasingly important role in present society, and suffers from the privacy leakage in plaintext. Therefore, a recognition system based on homomorphic encryption that supports privacy preservation is designed and implemented in this paper. This system uses the CKKS algorithm in the SEAL library, Microsoft’s latest homomorphic encryption achievement, to encrypt the normalized face feature vectors, and uses the FaceNet neural network to learn on the image’s ciphertext to achieve face classification. Finally, face recognition in ciphertext is accomplished. After been tested, the whole process of extracting feature vectors and encrypting a face image takes only about 1.712s in the developed system. The average time to compare a group of images in ciphertext is about 2.06s, and a group of images can be effectively recognized within 30 degrees of face bias, the identification accuracy can reach 96.71%. Compared with the face recognition scheme based on the Advanced Encryption Standard(AES) encryption algorithm in ciphertext proposed by Wang et al. in 2019, our scheme improves the recognition accuracy by 4.21%. Compared with the image recognition scheme based on Elliptical encryption algorithm in ciphertext proposed by Kumar S et al. in 2018, the total time in our system is decreased by 76.2%. Therefore, this scheme has better operational efficiency and practical value while ensuring the users’ personal privacy. Compared with the face recognition system in plaintext presented in recent years, our scheme has almost the same level on recognition accuracy and time efficiency.

scholarly journals The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

2022 ◽  
Author(s):  
Hang Du ◽  
Hailin Shi ◽  
Dan Zeng ◽  
Xiao-Ping Zhang ◽  
Tao Mei
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Face Alignment ◽  
Face Representation ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face ◽  
End To End

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals A FACIAL RECOGNITION USING OPEN COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.C K Gomathy ◽  
T. suneel ◽  
Y.Jeeevan Kumar Reddy
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Functional Space ◽  
Recognition System ◽  
Statistical Technique ◽  
Exciting Field ◽  
Two Dimensional Image ◽  
Face Recognition System ◽  
The Face ◽  
Amount Of Knowledge

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

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