A Comparative Study on PCA and KPCA Methods for Face Recognition

Mohammad Mohsen Ahmadinejad, Elizabeth Sherly

Abstract: An online face recognition system is a dynamic topic in the field of biometrics. The human face has a principal role which consists of complicated combination of features that allow us to communicate, express our feelings and emotions. Principal Components Analysis (PCA) and Kernel Principal Components Analysis (KPCA) are techniques that have been used in face feature extraction and recognition. In this paper, we have compared a PCA algorithm with KPCA algorithm, in which AT & T data set is used for comparison, recognition of accuracy, variation in facial expression, illumination changes, and computation time of each method. To find Recall of each algorithm AT & T database is used which shows that Kernel-PCA have better performance.

Keywords: PCA, KPCA, Face Recognition, Error Rates

How to Cite?: Mohammad Mohsen Ahmadinejad, Elizabeth Sherly, "A Comparative Study on PCA and KPCA Methods for Face Recognition", Volume 5 Issue 6, June 2016, International Journal of Science and Research (IJSR), Pages: 2589-2593, https://www.ijsr.net/getabstract.php?paperid=NOV164829, DOI: https://dx.doi.org/10.21275/NOV164829