Open Access
An Improved Face Recognition Approach using Principal Component Analysis
Deepak Gaur,Raj Kumar Sagar +1 more
- 01 Jan 2014
TL;DR: This research paper is going to represent the experimental results for facial recognition by using Principal Component Analysis (PCA) algorithm, and compare the results of research with these algorithm.
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Abstract: Due to digitization and for security purpose a lot of research has been going on in the wide area of affect computing. One of the field under this affect computing is to recognize the human faces with maximum accuracy. There are still large numbers of difficulties to recognize the accurate facial expression. In this research paper we are going to represent our experimental results for facial recognition by using Principal Component Analysis (PCA) algorithm. So in the first section of this paper we discussed some algorithm for facial recognition, than compare our results of research with these algorithm. We took Extended Cohn-Kanade Dataset(CK+) for experimental results. Our experimental results are implemented in OpenCV.
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References
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
Patrick Lucey,Jeffrey F. Cohn,Takeo Kanade,Jason Saragih,Zara Ambadar,Iain Matthews +5 more
- 13 Jun 2010
TL;DR: The Cohn-Kanade (CK+) database is presented, with baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data.
Face recognition using Laplacianfaces
TL;DR: Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
2.8K
Low-dimensional procedure for the characterization of human faces
Lawrence Sirovich,Michael Kirby +1 more
TL;DR: In this article, a method for the representation of (pictures of) faces is presented, which results in the characterization of a face, to within an error bound, by a relatively low-dimensional vector.
2.2K