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 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 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 Face Recognition System to RPA Software Design and Implementation

2020 ◽  
Vol 3 (2) ◽  
pp. 175-181
Author(s):  
Suwarno Suwarno
Keyword(s):  
Face Recognition ◽  
Software Design ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Process Automation ◽  
Face Recognition System ◽  
The Face ◽  
Python Programming ◽  
Client Side ◽  
Extract Information

The purpose of this research is to build a face recognition system, and implement it into an RPA (Robotic Process Automation) software to expand automation capabilities. The system is built using the Python programming language. The face recognition algorithm that is used is an open-source library that has been pre-trained and developed beforehand along with a library called OpenCV. The client side of the system is desktop based, and requires a stable internet connection. Users of the system are able to register faces into the system, and then later detect and extract information from them using only images of faces with an average speed of 500 ms for every frame, with an accuracy of ~98% with tolerance set at the default value of 0.6. The system is also capable of automatically registering any new faces that it encounters.

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).

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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.

Sign in / Sign up

Export Citation Format

Share Document