Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Component-Based Face Recognition with 3D Morphable Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Handbook of Face Recognition
Journal of Cognitive Neuroscience
A new ranking method for principal components analysis and its application to face image analysis
Image and Vision Computing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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The paper presents a problem of recognition of facial portraits in the aspect of benchmark database quality. The aim of the work presented here was to analyse the potential of datasets published over the Internet and the predicted applicability of such data for the task of face recognition performance verification. We gathered 41 datasets created and published by various academic and commercial bodies. In the paper we focus on both pure data characteristics, including the number of images, their spatial resolution, quality, content and usability, as well as more high-level properties, e.g. face orientation, expression, background, lighting, and attributes like hats, glasses and beards. We have chosen several datasets on which we performed more detailed experiments related to face recognition. We employed several database preparation algorithms (cross-validation based on different schemes) to make the results as much objective as possible. Here, Principal Component Analysis was employed, as a standard tool for dimensionality reduction. The classification was performed using simple Euclidean metrics. Performed experiments showed a true potential of selected databases.