A new extension to kernel entropy component analysis for image-based authentication systems

We introduce Feature Dependent Kernel Entropy Component Analysis (FDKECA) as a new extension to Kernel Entropy Component Analysis (KECA) for data transformation and dimensionality reduction in Image-based recognition systems such as face and finger vein recognition. FD- KECA reveals structure related to a new mapping space, where the most optimized feature vectors are obtained and used for feature extraction and dimensionality reduction. Indeed, the proposed method uses a new space, which is feature wisely dependent and related to the input data space, to obtain significant PCA axes. We show that FDKECA produces strikingly different transformed data sets compared to KECA and PCA. Furthermore a new spectral clustering algorithm utilizing FDKECA is developed which has positive results compared to the previously used ones. More precisely, FDKECA clustering algorithm has both more time efficiency and higher accuracy rate than previously used methods. Finally, we compared our method with three well-known data transformation methods, namely Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Kernel Entropy Component Analysis (KECA) confirming that it outperforms all these direct competitors and as a result, it is revealed that FDKECA can be considered a useful alternative for PCA-based recognition algorithms.