Real-Time Face Detection/Identification for

2010

Human face detection is an active area of research covering several disciplines such as image processing, pattern recognition and computer vision. This paper describes a face detection framework that is capable of processing input images pretty swiftly while achieving high detection rates. The existing methods for face detection can be divided into image based methods and feature based methods. The developed system is intermediary of these two, using a hybrid method comprising boosting algorithm and a hyper plane to train a classifier which is capable of processing images rapidly while having high detection rates. Using the response of simple Haar-based features used by Viola and Jones [1], AdaBoost algorithm and an additional hyper plane classifier, the presented face detection system is developed. This system is further modified by some intuitive noble heuristics. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems

cerc.wvu.edu

This paper presents a hardware implementation of face and eyes detection algorithm. To become we must go through several stages. First we have implemented an algorithm for detecting and tracking eyes written in C using an open source library of image processing and computer vision the "OpenCV". Then a profiling on HW/SW (hardware/software) partition was done. The hardware part solves the part of the algorithm with higher computational costs. The system has been implemented on Spartan3A DSP FPGA board. In order to visualize the results on real images, we have made a co-simulation of this bloc using the Simulink tool of Matlab.

In the fast moving modern world everything is changed to provide a better life for humanity. New developments make this as a reality. So we were decided to develop the Real Time Face detection and recognition system. The importance of the automatic face detection and tracking system has increased as it is needed for video surveillance and new user interfaces. And providing higher security to the country. This system suitable for all people, but some people’s face damaged on acid attack, war and etc. so we were used to password for these peoples. In this research our effort is to develop a system for face detection and recognition in the real time. That will be effiient and give solution for many problems. The system was developed using C# .Net programming, Viola-Jones algorithm (Haar Cascade Classifir), PCA (classifid as either Feature based and image based) and EmguCV (Computer vision Library and wrapper class of Open CV)

Face Detection and Recognition of Multiple Faces within a Single Surveillance Area, 2010

This paper proposes incorporation of face detection and recognition algorithms into video surveillance systems, so that known suspects can be identified. With this functionality,surveillance systems become pro-active, thus assisting the person that attends them. Haar cascade classifier and Eigenfaces from OpenCV library are implemented for face detection and recognition, respectively. Three variants of face classification were experimented with during prototype development, namely: first neighbor, majority count and average distance. After the development phase, a series of tests were conducted in order to assess prototype correctness,determine the most suitable threshold for positive identification, measure CPU and memory usage and detect possible changes in camera frame rate.

IJRCAR, 2014

Emerging smart visual surveillance systems are requiring automatic detection and recognition of human beings within the scene and the prediction of the actions being performed by the detected human objects. These are challenging issues especially in an unconstrained environment. This paper presents a framework for the automatic detection and recognition of human beings from video cameras via smart visual systems that automatically sense and correctly recognize human identity and actions by means of Machine vision techniques. Such systems require low response time in terms of image processing and acceptable recognition accuracy. Initial human detection is addressed by background subtraction techniques using parallel–processed Kernel Density Estimation (PKDE). Temporal tracking of the objects’ trajectories is performed by employing a spatial body tracking system designed as a multi-part colour histogram-based tracker. In face recognition, the Principal Component Analysis (PCA) algorithm was implemented. An experiment was performed in computer laboratory of Federal University of Technology Akure where a camera was installed in the Laboratory to capture students entering the lab. The face detection algorithm performs well and reduces the computational time. The detector is 2.5 times as fast as Viola and Jones method, although there were false positive faces detected. Some future areas of practical application of such system include access control to facilities like lecture rooms, Automated Teller machines, and attendant management systems.

2016

Face recognition has been an important and active research field because it offers many applications. A real time face recognition algorithm is proposed in this paper based on the use of Haar wavelet decomposition and the principle of Principle Component Analysis (PCA). The aim of this paper is to reduce the algorithm complexity to achieve the time requirement of real time. To get better recognition rate, feature normalization method is proposed based on the computation of mean and standard deviation for the partially feature extracted. Also the faces are classified using the proposed MCF algorithm. The system accuracy is reaches to 100% with time consumed about 0.03 second.

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...

This paper introduces a novel techniqueto detect faces similarly recognizes in real-time with very high rate. It is essentially a feature-based approach, in which a classifier is trained for Haar-like rectangular features selected by AdaBoost algorithm and efficient representation method histogram equalization is used for varying illumination in the image. The face detection system generates an integral image window to perform a Haar feature classification during one clockcycle. And then it performs classification operations inparallel using Haar classifiers to detect a face in the image sequence. The classifiers in the beginning of the cascade are simpler and consist of smaller numbers of features. Although a face detection module is typically designed to deal with single images, its performance can be further improved if video stream is available. However, as one proceeds in the cascade, the classifiers become more complex. A region is reported as detection only if it passes all the classifier stages inthe cascade. If it is rejected at any stage, it is discardedand not processed further. If all stages are passed the face of a candidate is concluded to be recognized face.

International journal of engineering research and technology, 2013

In this paper, we present information about the face detection and tracking system with real time video as an input. The working method of this system is entirely divided into two main modules. The face recognition and detection from the video is the first module while the tracking is the second module. To detect the face in the image, Face Name Graph Matching algorithm is used. This algorithm involves various methods such as Haar-Cascade method, Open-cv libraries etc. Face clustering algorithm is used for tracking the face in the video. This system is mainly designed for the security purposes. Video recorded in public areas for known person or suspicious activities are used as an input for the system. System will identify and track one or more people captured in that video, this is taken as an output of this system. This paper involves working of face tracking system, algorithms which are involve and results of the system. KEYWORDSFace detection, Face recognition, Face tracking, Fa...

—This paper describes about computer vision for localizing the faces and determining the sizes of faces in real-time in full-HD video. It is visualized through Machine Learning approach and the subsequent idea is to implement the same framework on the latest big data analytics tools for achieving face detection in online fashion. Here, Viola-Jones algorithm is considered for implementation. It is based on technique called Haar features. Different size and types of Haar features have resemblance to different facial regions representing distinct characteristics. Integral Image makes algorithm computationally efficient and AdaBoost reduces feature vector into only essential features that captures overall behavior. Cascading includes stages of classifiers to match the extracted features with actual characteristics and decision making about the face. Keywords—Face detection, Viola-Jones algorithm (Haar features + Integral Image + AdaBoost + Cascading)