A real-time face detection and recognition system

There is a need for the automatic monitoring of an environment for surveillance purposes, attendance management and access control. This automatic monitoring system becomes very meaningful if coupled with the ability to identify persons present in such environment. Most surveillance and monitoring systems are basically video cameras or CCTVs which give data in form of videos and images. The process of identification of persons in the videos or images can be taken care of through the detection and recognition of faces present in the images. This project states the design of a face detection and recognition system implemented with MATLAB. Viola Jones algorithm was employed for the detection of faces. The system based on Viola Jones algorithm was slow during implementation, hence this research work attempts to enhance the speed of detection. Principal Component Analysis (PCA) was employed for dimensionality reduction, and the matching correlation analysis for matching. The performance of the system was measured based on the detection speed and recognition efficiency, the system was tested with 88 test images. Case studies were taken for the performance of the system with varying skin color and with occlusion.

International Journal of Trend in Scientific Research and Development, 2018

Face Detection is concerned with finding whether or not there are any faces in a given images. Security and surveillance are the two important aspects of human being. Face detection is very important because it is not being safe in human environment. So, F Detection Security System is essential between individual in life. In the modern world everything is changed to provide a better life. So we were decided to develop the Real Time Face detection system. The importance of the face detection as it is esse surveillance and real user interfaces security to the country. Face differ in skin colour, nose, eyebrows, chin between different people in humanity. In this paper we effort is to develop system about face detection security system in the real time.

2021

In this study, a system for real-time face recognition was built using the Open Face tools of the Open CV library. The article describes the methodology for creating the system and the results of its testing. The Open CV library has various modules that perform many tasks. In this paper, Open CV modules were used for face recognition in images and face identification in real time. In addition, the HOG method was used to detect a person by the front of his face. After performing the HOG method, 128 face measurements were obtained using the image encoding method. A convolutional neural network was then used to identify people's faces using the SVM linear classifier algorithm.

The demand of smart cameras for various applications in surveillance for security purpose is growing rapidly. However till now, the handling required for real time face detection prohibits integration of the complete application into a small sized camera. The Real Time Face Tracking and Identification for Surveillance System proposed in this research work has originated ahead with a simple and an efficient approach to satisfy the handling requirements and enable constant and precise face detection. The core functionality of Real Time Face Tracking and Identification for Surveillance System is to allow tracking and recognition of human faces in a video stream and thereby provide a centralized, cost effective and robust mechanism of securing business and government premises. The primary objective of the proposed system is to reduce detection redundancy, minimize misclassification risk, provide huge processing capacity and work within a reasonable computational budget.

Intelligent Decision Technologies, 2017

The ability to automatically recognize human faces based on dynamic facial images is important in security, surveillance and the health/independent living domains. Specific applications include access control to secure environments, identification of individuals at a particular place and intruder detection. This research proposes a real-time system for surveillance using cameras. The process is broken into two steps: (1) face detection and (2) face recognition to identify particular persons. For the first step, the system tracks and selects the faces of the detected persons. An efficient recognition algorithm is then used to recognize detected faces with a known database. The proposed approach exploits the Viola-Jones method for face detection, the Kanade-Lucas-Tomasi algorithm as a feature tracker and Principal Component Analysis (PCA) for face recognition. This system can be implemented at different restricted areas, such as at the office or house of a suspicious person or at the entrance of a sensitive installation. The system works almost perfectly under reasonable lighting conditions and image depths.

Advances in Transdisciplinary Engineering

Face detection and recognition can be applied to numerous fields, and it is primarily used for improving security. For security purposes, facial recognition is considered to be the most reliable and accurate technology for identifying a person. Improvements in security systems can be made through this technology without causing any inconvenience. This article discusses several systems, such as smart home security systems, autonomous face detection systems, automotive security-based systems, face detection for surveillance applications, and multi-face recognition systems. Various detection mechanisms include such as Local Binary Pattern Histogram (LBPH), Support Vector Machine (SVM), AdaBoost learning algorithm, Haar Classifier Algorithm, and Principal component Analysis (PCA). A detailed study is carried out with these advanced techniques and their advantages, disadvantages and accuracies are compared and contrasted. According to the investigations, the Haar classifier appears to be...

Head of the Department Mr. M.APPARAO, M.TECH, M.B.A,(PH.D) PROFESSOR&HOD ACKNOWLEDGEMENT I thank the almighty for giving us the courage and perseverance in completing the mainproject.This project itself is acknowledgements for all those people who have give ustheir heartfelt co-operation in making this project a grand success. I extend our sincere thanks to Mr.M.VENU GOPAL RAO, B.E, M.B.A, D.M.M. Chairman of our college, for providing sufficient infrastructure and good environment in the College to complete our course. I am thankful to our secretary Mr. M. SRIDHAR, M.Tech, for providing the necessary Infrastructure and labs and also permitting to carry out this project. I am thankful to our principal Dr.C.V.SUBBA RAO, B.Tech, M.E, Ph.D, MISTE for providing the necessary infrastructure and labs and also permitting to carry out this project. With extreme jubilance and deepest gratitude, I would like to thank Head of the E.C.E. Department, Mr.M.APPARAO,M.Tech, MBA, (Ph.D) for his constant encouragement. I special thanks to our Project coordinator Mr.B.SIVA PRASAD, M.Tech, Associate Professor, Electronics and Communications engineering, for his support and valuable suggestions regarding project work. I am greatly indebted to project guide Ms.SK.AYESHA, M.Tech, Assistant Professor, Electronics and Communications engineering, for providing valuable guidance at every stage of this project work. I am profoundly grateful towards the unmatched services rendered by him.

JOIV : International Journal on Informatics Visualization, 2017

Authentication is the process of verifying one’s identity, and one of its implementation is in taking attendances in university’s lectures. Attendance taking is a very important matter to every academic institution as a way to examine students’ performance. Signature based attendance taking can be manipulated. Therefore it has problems in verifying the attendance validity. In this final project, a real time eigenface based face recognition is implemented in an application to do attendance taking. The input face image is captured using a webcam. The application itself is built in C#, utilizing EmguCV library. The application is developed using Visual Studio 2015. Face detection is done with Viola-Jones algorithm. The eigenface method is used to do facial recognition on the detected face image. In this final project, a total of 8 testings are done in different conditions. From the testings, it is found that this application can recognize face images with accuracy as high as 90% and as...

2016

Face processing is one among the popular problems of detection and recognition. In surveillance system application, the massive data of video is analyzed by complex algorithms that must consider the real-time constraints. In this paper we propose a face processing framework as a component-based architecture for dealing with face online processing. Face detection and classification algorithms are realized based on a Haar-like features and Support Vector Machine (SVM) respectively as a way to efficiently extract a face image and enhance face recognition rates with robustness on different orientation Experimental results are presented with discussion. 1.

A real and accurate object detection and Recognition technique is used to detect the real objects in the human face such as eye, nose etc…The face recognition is applicable to detect only the exact human face. But it does nose recognize the human face if there is any damage found in the face. To overcome these problem implement the Bayesian algorithm is used to detect the objects on human face like eye, nose etc... By using these algorithm the correct authorized user can be identified by comparing the current pixel values of the real object with the pixel values already stored in the database This system use C4 algorithm along with the MSRF (Multi Scale Random Field) because these methods are used to calculate only the neigbhouring pixel value by providing the fast rejection on the background image .Grayscale conversion from an RGB value results in the easy calculation of the pixel value. Pixel by pixel calculation is done and provides the secure authorization for the user against the hackers because the pixel value of an particular authorized person is entirely differ from the hackers, which can be get detected by SMAP (Sequential MAP)while handling transaction in an ATM Machine.