IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Unified model in identity subspace for face recognition
Journal of Computer Science and Technology - Special issue on computer graphics and computer-aided design
The theoretical analysis of GLRAM and its applications
Pattern Recognition
On the Dimensionality of Face Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Covariance estimation in full- and reduced-dimensionality image classification
Image and Vision Computing
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Personal photo enhancement using example images
ACM Transactions on Graphics (TOG)
Face detection using an SVM trained in eigenfaces space
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face hallucination and recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Optimal regions for linear model-based 3D face reconstruction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
An improvement on PCA algorithm for face recognition
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Improvement on PCA and 2DPCA algorithms for face recognition
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A survey of face hallucination
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Multi-resolution feature fusion for face recognition
Pattern Recognition
| Hi-index | 0.00 |
Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has been used in the past to derive such compact representations for the object class of human faces. Here, with an interpretation of PCA as a probabilistic model, we employ two objective criteria to study its generalization properties in the context of large frontal-pose face databases. We find that the eigenfaces, the eigenspectrum, and the generalization depend strongly on the ensemble composition and size, with statistics for populations as large as 5500, still not stationary. Further, the assumption of mirror symmetry of the ensemble improves the quality of the results substantially in the low-statistics regime, and is also essential in the high-statistics regime. We employ a perceptual criterion and argue that, even with large statistics, the dimensionality of the PCA subspace necessary for adequate representation of the identity information in relatively tightly cropped faces is in the 400-700 range, and we show that a dimensionality of 200 is inadequate. Finally, we discuss some of the shortcomings of PCA and suggest possible solutions.