Abstract
Objectives: A simple and efficient method is employed to extract feature vector from images and to reduce the dimension of data. Once the feature vectors are extracted, it can be used in face recognition module. Methods/Analysis: Principal Components Analysis (PCA) is used for face recognition technique for feature identification in large data sets and to highlight their similarities and differences is more essential step in face recognition. PCA is used as efficient tool in data analysis to reduce dimension and to obtain maximum variance of data. Findings: Experiment is conducted using Yale database B. The face images are formed with multiple factors on different lighting conditions, background interference, and face rotation etc. The experimental results on Yale database B are given to illustrate the proposed method. Conclusion/Application: A simple and effective feature extraction method is analyzed for face images and experimental results are shown. Keywords: Eigenvalue, Eigenvector, Feature Vector Extraction, Face Recognition, Principal Component Analysis
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
