Comparison of PCA and 2D-PCA on Indian Faces
- Resource Type
- Conference
- Authors
- Rajendran, Sekhar; Kaul, Amit; Nath, Ravinder; Arora, A. S.; Chauhan, Sushil
- Source
- 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014) Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on. :561-566 Jul, 2014
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Signal Processing and Analysis
Principal component analysis
Training
Image recognition
Face recognition
Biomedical imaging
Hair
Eigenfaces
face recognition
Indian faces
PCA
Two-Dimensional PCA
2D-PCA
Preprocessing techniques
unsupervised statistical feature extraction
- Language
Face recognition is an extensively researched topic by researchers from diverse disciplines. Several unsupervised statistical feature extraction methods have been used in face recognition, out of these in this paper a comparison of the PCA(eigenfaces) and 2D-PCA approaches on Indian Faces has been presented. To test and compare their performances a series of experiments were performed on ORL database, Yale face database and then on an in-house dataset which has been collected over a span of 6 months. The performance parameters compared here are recognition rate and speed with varying number of training images. The application of various preprocessing techniques which can be used to improve their performance has also been studied.