scholarly journals Multimodal verge for scale and pose variant real time face tracking and recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 665
Author(s):  
Ramkumar Govindaraj ◽  
E. Logashanmugam
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Real Time ◽  
Research Field ◽  
Face Tracking ◽  
Low Rank ◽  
Facial Features ◽  
The Face ◽  
Low Rank Representation ◽  
Tracking And Recognition

In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.

Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

2013 ◽  
Vol 373-375 ◽  
pp. 442-446
Author(s):  
Hai Feng Sang ◽  
Chao Xu ◽  
Dan Yang Wu ◽  
Jing Huang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Research Field ◽  
Face Tracking ◽  
Fusion Algorithm ◽  
The Real ◽  
Video Images ◽  
Tracking And Recognition

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

scholarly journals Typographic Portrait Generator Using Image Processing-Project Application

2021 ◽  
Vol 9 (10) ◽  
pp. 1712-1716
Author(s):  
Prof. V.V. Waykule
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Face Recognition ◽  
Edge Detection ◽  
Facial Features ◽  
Visual Appeal ◽  
Word Clouds ◽  
The Face ◽  
Distinct Features

Abstract: Typography portrait is a portrait made up of words. The face of a person has different shades and distinct features. In order to make a typography portrait the distinct shades and features need to be clearly extracted. Existing systems focus only on the different shades of the face. They fill the face with word clouds without any regard for the distinct features of a face. This project will consider not only the shades of a face but also the features of a person’s face. The extraction of facial features will be done first and then text filling will be done accordingly. This project intends to create an application that will create a typography portrait having a much better visual appeal than the present systems. Keywords: Image Processing, Edge Detection, Image Segmentation, Face Recognition, Text Filling.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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 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.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

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