Enhanced face recognition through variation of Principle Component Analysis (PCA)

No abstract is available for this paper.

• Publication year

  2007

• Venue

  International Conference on Industrial and Information Systems

• Publication date

  2007-08-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/ICIINFS.2007.4579200
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Face Recognition: A Literature Review

  2008cited by this paper
• A novel method for face recognition under partial occlusion or facial expression variations

  2005cited by this paper
• Face Recognition Using DCT and Hybrid Flexible Neural Tree

  2005cited by this paper
• Independent comparative study of PCA, ICA, and LDA on the FERET data set

  2005cited by this paper
• A hybrid face recognition method using Markov random fields

  2004cited by this paper
• A Statistical Assessment of Subject Factors in the PCA Recognition of Human Faces

  2003cited by this paper
• Recognizing faces with PCA and ICA

  2003cited by this paper
• Analysis of Recognition Algorithms using Linear , Generalized Linear , and Generalized Linear Mixed Models

  2003cited by this paper
• Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures

  2003cited by this paper
• The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure

  2003cited by this paper
• PCA vs. ICA: A Comparison on the FERET Data Set

  2002cited by this paper
• The CMU Pose, Illumination, and Expression (PIE) database

  2002cited by this paper
• Face recognition using mixtures of principal components

  2002cited by this paper
• Independent component analysis for face authentication

  2002cited by this paper
• Meta-analysis of face recognition algorithms

  2001cited by this paper
• A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition

  2001cited by this paper
• An Efficient LDA Algorithm for Face Recognition

  2000cited by this paper
• Frontal View Human Face Detection and Recognition

  2000cited by this paper
• Face Recognition: a Summary of 1995 - 1997

  1997cited by this paper
• The FERET evaluation methodology for face-recognition algorithms

  1997cited by this paper
• Algorithm evolution for face recognition: what makes a picture difficult

  1995cited by this paper
• The recognition of human faces by computer

  1993cited by this paper
• A face recognition system using fuzzy logic and artificial neural network

  1992cited by this paper

• Parallel deep learning architecture with customized and learnable filters for low-resolution face recognition

  2023cites this paper
• Single-User 2D Gaze Estimation in Retail Environment Using Deep Learning

  2022cites this paper
• An effective face recognition method using guided image filter and convolutional neural network

  2021cites this paper
• Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos

  2021cites this paper
• ROBUST FACE RECOGNITION WITH AGE GROUP ESTIMATION

  2020cites this paper
• A Comparative Study of Face Recognition System Using PCA and LDA

  2013cites this paper
• PCA-LDA based face recognition system & results comparison by various classification techniques

  2013cites this paper
• Dual Tree Complex Wavelet Transform for Face Recognition Using PCA Algorithm

  2013influential citation
• Face Recognition Using PCA and Bit-Plane Slicing

  2013cites this paper
• Hybrid Feature-Based Teeth Recognition

  2012cites this paper
• Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system

  2011cites this paper
• Hybrid feature-based teeth recognition system

  2011cites this paper
• Suitability of Complex Wavelets towards Face Recognition

  2011cites this paper
• Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system

  2011cites this paper