Eigenspectra Palmprint Recognition
Moussadek Laadjel, Ahmed Bouridane, Fatih Kurugöllü · 2008
This paper proposes a novel technique for palmprint recognition in the transform domain and based on a combination of Principal Component Analysis (PCA) and Fourier transform. Although, PCA is widely adopted as one of the most promising tools for use in biometric recognition systems, it comes with its own limitations: poor discriminating power in the presence of variant illuminations, requires a large computational load when the original dimensionality of data is high while the number of training samples is usually large. Traditionally, to represent the palmprint image, PCA is carried out on the whole spatial image. In the proposed method, Fourier transform is used to decompose an image into its sine and cosine components, then the spectrum is used for PCA representation since doing PCA on the whole frequency domain does not achieve any performance improvements. In comparison with the traditional PCA and three other methods, the proposed method yields better recognition accuracy and discriminating. In addition, the proposed method reduces the computational load significantly to half due to the symmetry property of Fourier transform.