In this paper we propose a prototype of a near real-time automatic face recognition system that is aimed for operations in a less constrained environment, using the earliest appearance-based face recognition technique called eigenfaces. The main idea of eigenfaces is to decompose face images into a small set of significant characteristic feature images called eigenvectors, which are the princip...

In this project, we are given a list of 96×96-pixel 8bit graylevel images with their corresponding (x,y) coordinates of 15 facial keypoints. We first adopt hold-out cross validation to randomly split the data set into a training set and a test set, so that we can develop our algorithm on the training set and assess its performance on the test set. Our algorithm first performs histogram stretchi...

This paper presents a video coding techique that achieves high visual quality at very low bit rates. Each video frame is divided into two regions, consisiting of a background area and a visually important feature to be coded at higher bit rates. The feature is tracked from frame to frame and it is coded using a set of features that are extracted from a training set. The set of features, which w...

This paper introduces a Bayesian framework for face recognition which unifies popular methods such as the eigenfaces and Fisherfaces and can generate two novel probabilistic reasoning models (PRM) with enhanced performance. The Bayesian framework first applies Principal Component Analysis (PCA) for dimensionality reduction with the resulting image representation enjoying noise reduction and enh...

This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher s Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. 2004 Elsevier B.V. All rights reserved.