scispace - formally typeset
Search or ask a question
Topic

Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.


Papers
More filters
Proceedings ArticleDOI
10 Dec 2002
TL;DR: This work proposes embedding the super-resolution algorithm into the face recognition system so that super- resolution is not performed in the pixel domain, but is instead performed in a reduced dimensional domain.
Abstract: Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed that attempt to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing system to obtain a high resolution image that can later be passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose embedding the super-resolution algorithm into the face recognition system so that super-resolution is not performed in the pixel domain, but is instead performed in a reduced dimensional domain. The advantage of such an approach is a significant decrease in the computational complexity of the super-resolution algorithm because the algorithm no longer tries to construct a visually improved high quality image, but instead constructs the information required by the recognition algorithm directly in the lower dimensional domain without any unnecessary overhead.

29 citations

Journal ArticleDOI
TL;DR: Perceptual studies suggesting that dependency learning is relevant to human face perception as well are reviewed, and an information maximization account of perceptual effects such as the atypicality bias, and face adaptation aftereffects is presented.

29 citations

Proceedings ArticleDOI
17 Oct 2005
TL;DR: The models and methods developed have applications to person recognition and face image indexing and a multidimensional representation of hair appearance is presented and computational algorithms are described.
Abstract: We develop computational models for measuring hair appearance for comparing different people. The models and methods developed have applications to person recognition and face image indexing. An automatic hair detection algorithm is described and results reported. A multidimensional representation of hair appearance is presented and computational algorithms are described. Results on a dataset of 524 subjects are reported. Identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair based identification.

29 citations

Journal ArticleDOI
TL;DR: In this article, multiscale techniques are used to partition the information contained in the frequency domain prior to dimensionality reduction to increase the information available for classification and increase the discriminative performance of both eigenfaces and fisherfaces techniques.
Abstract: The eigenfaces algorithm has long been a mainstay in the field of face recognition due to the high dimensionality of face images. While providing minimal reconstruction error, the eigenface-based transform space de-emphasizes high-frequency information, effectively reducing the information available for classification. Methods such as linear discriminant analysis (also known as fisherfaces) allow the construction of subspaces which preserve the discriminatory information. In this article, multiscale techniques are used to partition the information contained in the frequency domain prior to dimensionality reduction. In this manner, it is possible to increase the information available for classification and, hence, increase the discriminative performance of both eigenfaces and fisherfaces techniques. Motivated by biological systems, Gabor filters are a natural choice for such a partitioning scheme. However, a comprehensive filter bank will dramatically increase the already high dimensionality of extracted features. In this article, a new method for intelligently reducing the dimensionality of Gabor features is presented. The face recognition grand challenge dataset of 3-D face images is used to examine the performance of Gabor filter banks for face recognition and to compare them against other multiscale partitioning methods such as the discrete wavelet transform and the discrete cosine transform.

29 citations

Journal ArticleDOI
01 Feb 2014
TL;DR: Fuzzy measures are of particular interest with this regard given their monotonicity property, which stands in a clear contrast with the more restrictive additivity property inherent to probability–like measures.
Abstract: People recognize familiar faces in a similar way by using interior facial features (facial regions) such as eyes, nose, mouth, etc. However, the importance of these regions in the realization of face identification and a quantification of the impact of such regions on the recognition process could vary from one region to another. An intuitively appealing observation is that of monotonicity: the more regions are taken into account in the recognition process, the better. From a formal point of view, the relevance of the facial regions and an aggregation of these pieces of experimental evidence can be described in the formal setting of fuzzy measures. Fuzzy measures are of particular interest with this regard given their monotonicity property (which stands in a clear contrast with the more restrictive additivity property inherent to probability---like measures). In this study, we concentrate on the construction of fuzzy measures (more specifically, $$ \lambda $$ ? -fuzzy measure) and characterize their performance in the problem of face recognition using a collection of experimental data.

29 citations


Network Information
Related Topics (5)
Feature (computer vision)
128.2K papers, 1.7M citations
87% related
Feature extraction
111.8K papers, 2.1M citations
86% related
Image segmentation
79.6K papers, 1.8M citations
85% related
Convolutional neural network
74.7K papers, 2M citations
83% related
Deep learning
79.8K papers, 2.1M citations
82% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202249
202120
202043
201953
201840