Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

Detekcia a identifikácia osôb v objektoch kritickej infraštruktúry dopravných stavieb

This paper focuses on identification of persons entering objects of crucial infrastructure and subsequent detection of movement in parts of objects. It explains some of the technologies and approaches to processing specific image information within existing building apparatus. The article describes the proposed algorithm for detection of persons. It brings a fresh approach to detection of moving objects (groups of persons involved) in enclosed areas focusing on securing freely accessible places in buildings. Based on the designed algorithm of identification with presupposed utilisation of 3D application, motion trajectory of persons in delimited space can be automatically identified. The application was created in opensource software tool using the OpenCV library.Tento príspevok sa zaoberá problematikou identifikácie osôb vstupujúcich do objektov kritickej infraštruktúry a následnou detekciou ich pohybu v častiach objektov. Ozrejmuje niektoré technológie a postupy, ktoré sú pri spracovaní špecifickej obrazovej informácie, kde je použité stávajúce vybavenie budov. V článku je ďalej opísaný navrhnutý algoritmus na detekciu osoby. Prináša nový pohľad na detekciu pohybujúcich sa objektov (záujmových skupín osôb) na uzavretých plochách, pričom kladie dôraz na zabezpečenie voľne prístupných miest v budovách. Na základe navrhnutého algoritmu identifikácie s predpokladaným využitím 3D aplikácie je možné automatizovane identifikovať trajektóriu pohybu osoby vo vymedzenom priestore. Aplikácia bola vytvorená v opensourse softvérovom prostriedku s využitím knižnice OpenCV

Real time face matching with multiple cameras using principal component analysis

Face recognition is a rapidly advancing research topic due to the large number of applications that can benefit from it. Face recognition consists of determining whether a known face is present in an image and is typically composed of four distinct steps. These steps are face detection, face alignment, feature extraction, and face classification [1]. The leading application for face recognition is video surveillance. The majority of current research in face recognition has focused on determining if a face is present in an image, and if so, which subject in a known database is the closest match. This Thesis deals with face matching, which is a subset of face recognition, focusing on face identification, yet it is an area where little research has been done. The objective of face matching is to determine, in real-time, the degree of confidence to which a live subject matches a facial image. Applications for face matching include video surveillance, determination of identification credentials, computer-human interfaces, and communications security. The method proposed here employs principal component analysis [16] to create a method of face matching which is both computationally efficient and accurate. This method is integrated into a real time system that is based upon a two camera setup. It is able to scan the room, detect faces, and zoom in for a high quality capture of the facial features. The image capture is used in a face matching process to determine if the person found is the desired target. The performance of the system is analyzed based upon the matching accuracy for 10 unique subjects

Principles and methods for face recognition and face modelling

This chapter focuses on the principles behind methods currently used for face recognition, which have a wide variety of uses from biometrics, surveillance and forensics. After a brief description of how faces can be detected in images, we describe 2D feature extraction methods that operate on all the image pixels in the face detected region: Eigenfaces and Fisherfaces first proposed in the early 1990s. Although Eigenfaces can be made to work reasonably well for faces captured in controlled conditions, such as frontal faces under the same illumination, recognition rates are poor. We discuss how greater accuracy can be achieved by extracting features from the boundaries of the faces by using Active Shape Models and, the skin textures, using Active Appearance Models, originally proposed by Cootes and Talyor. The remainder of the chapter on face recognition is dedicated such shape models, their implementation and use and their extension to 3D. We show that if multiple cameras are used the the 3D geometry of the captured faces can be recovered without the use of range scanning or structured light. 3D face models make recognition systems better at dealiing with pose and lighting variatio

Real-Time Face Recognition Using Eigenfaces

In recent years considerable progress has been made in the area of face recognition. Through the development of techniques like eigenfaces, computer can now compute favourably with humans in many face recognition tasks, particularly those in which large databases of faces must be searched. Whilst these methods perform extremely well under constrained conditions, the problem of face recognition under gross variations in expressions, view and lighting remains largely unsolved. This paper details the design of a real-time face recognition system aimed at operating in less constrained environments. The system is capable of single scale recognition with an accuracy of 94% at 2 frames per second. A description of the system's performance and the issues and problems faced during its development is given

Assisting Blind Using Facial Recognition and Voice Assistance

The paper aims at developing a system that would be able to recognize the face and hence play the recorded voice of the recognized face (e.g. Playing name of the person recognized ) using speaker/headset and voice module. The paper shows the usage of an electronic gadget in which a software is programmed, the usage of such kinds of product have increased many folds in the past decade because of the advancement in electronic technology as well as in the field of software development. We have used PCA technology with implementation of eigenfacesto recognize faces of different individuals. MATLAB was predominantly used to apply this algorithm. A voice module connected to Arduino uno plays the recorded voice of the identified individual. This could be of further importance for users who are blind, so that they are informed of the person at their door

Face detection and clustering for video indexing applications

This paper describes a method for automatically detecting human faces in generic video sequences. We employ an iterative algorithm in order to give a confidence measure for the presence or absence of faces within video shots. Skin colour filtering is carried out on a selected number of frames per video shot, followed by the application of shape and size heuristics. Finally, the remaining candidate regions are normalized and projected into an eigenspace, the reconstruction error being the measure of confidence for presence/absence of face. Following this, the confidence score for the entire video shot is calculated. In order to cluster extracted faces into a set of face classes, we employ an incremental procedure using a PCA-based dissimilarity measure in con-junction with spatio-temporal correlation. Experiments were carried out on a representative broadcast news test corpus

Biometrics: Facial Recognition

Biometrics, a biological measurement and refers to the automatic identification of a person based on his or her physiological or psychological characteristics. This project defines biometrics and its application in the real world focusing the study on facial recognition. Facial recognition is the identification of an individual based on the facial data characteristics such as facial features and face position. The objective of this project is to develop a program which could verify a face when compared to a database of known faces by using MATLAB. This project also explains the details of facial recognition especially on the four main facial recognition categories and other components related in characterizing a face. It also lists the various approaches in handling facial recognition where we see different methods applied and opinions on which method is better and what factors influenced them. Among the various approaches, eigenface technique is explained in detail including its work procedure, algorithm and the tools applied. Main part of this project discussed on the project findings. The result and output produced is elaborated according to the sequence of the program developed where many face images displayed. Finally, this project reviews the relevancy of the study contents with the objectives and listed out some recommendation for further work expansion