Topic
Eigenface
About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.
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TL;DR: This paper proposes a robust face recognition approach by means of learning a class-specific dictionary and a projection matrix and shows that the proposed algorithm performs better than some state-of-the-art methods.
9 citations
TL;DR: A real time face detection and recognition system which uses an “appearance-based” approach and worked with Eigen Faces which is a PCA based algorithm.
Abstract: We tried to develop a real time face detection and recognition system which uses an “appearance-based” approach. For detection purpose we used Viola Jones algorithm. To recognize face we worked with Eigen Faces which is a PCA based algorithm. In a real time to recognize a face we need a data training set. For data training set we took five images of each person and manipulated the Eigen values to match the known individual.
9 citations
21 Jan 2014
TL;DR: From the experiment conducted, the PCA eigenfaces approach is able to deliver and produce high accuracy results and can recognize faces in a single static, multiple static and dynamic images.
Abstract: Face recognition is an ever changing and evolving domain for research. However, the application of face recognition is still new and not commonly used in this country as opposed to other biometric identifications. This paper seeks to find out the effectiveness and weaknesses of face recognition approach known as the eigenfaces. It was conducted using the PCA algorithm on eigenfaces on 35 students using different images stored in a training database. From the experiment conducted, the PCA eigenfaces approach is able to deliver and produce high accuracy results. It can recognize faces in a single static, multiple static and dynamic images.
9 citations
08 Jun 2017
TL;DR: Face recognition in this paper is based on Eigenface algorithm, using pixel information from images captured by webcam, and the image is represented using PCA method to calculate its performance with sensitivity, specificity, and accuracy.
Abstract: Attendance is the documentation of presence and activity in institution. A software has been made to monitor the attendance using face recognition. The software uses camera to capture the image and works on any background color. The aim of this paper is to calculate its performance with sensitivity, specificity, and accuracy using Eigenface Algorithm and Principal Component Analysis (PCA) method. Face recognition in this paper is based on Eigenface algorithm, using pixel information from images captured by webcam. The image is represented using PCA method. The software is tested using different expressions and accessories in object’s face. The performance of the software indicates 73.33%sensitivity, 52.17% specificity, and 86.67% accuracy. The successful rate in identifying the face for distance testing is 70%, while successful rate of 85% is achieved for object wearing eyeglasses and veil (jilbab). Furthermore, the successful rate for various expression is 85.33%.
9 citations
12 Jul 2009
TL;DR: This paper proposes a new method of feature extraction for face recognition based on descriptive statistics of a face image that yields a higher recognition rate and requires less computational time than the Eigenface method.
Abstract: This paper proposes a new method of feature extraction for face recognition based on descriptive statistics of a face image. Our method works by first converting the face image with all the corresponding face components such as eyes, nose, and mouth to a grayscale images. The features are then extracted from the grayscale image, based on a descriptive statistics of the image and its corresponding face components. The edges of a face image and its corresponding face components are detected by using the canny algorithm. In the recognition step, different classifiers such as Multi Layer Perceptron (MLP), Support Vector Machine (SVM), k -Nearest Neighbors (k-NN) and Pairwise Opposite Class-Nearest Neighbor (POC-NN) can be used for face recognition. We evaluated our method with more conventional Eigenface method based upon the AT&T and Yale face databases. The evaluation clearly confirm that for both databases our proposed method yields a higher recognition rate and requires less computational time than the Eigenface method.
9 citations