This study focuses on the development of a human identification system using eigenpalm images. Human identification based on biometric technology is extensively used in several applications, such as access control and criminal investigation. The proposed method consists of three main stages. The preprocessing stage computes the palmprint images to capture important information and produce a better representation of palmprint image data. The second stage extracts significant features from palmprint images and reduces the dimension of the palmprint image data by applying the principal component analysis (PCA) technique. A linear projection method is used in this stage to reduce redundant features and remove noise from the palmprint image. Furthermore, this approach increases discrimination power in the feature space. The Euclidean distance classifier is used in the classification stage, which is the third stage. The proposed method is tested using a benchmark PolyU dataset. Experimental results show that the best achieved recognition rate is 97.5% when the palmprint image is resized with 0.2 resizing scale and represented using 34 PCA coefficients. The raw data projection and Euclidean distance classifier can be implemented on a digital signal processor (DSP) board. Implementing the proposed algorithm using the DSP board achieves better performance in computation time compared with a personal computerbased system which make the system 47.2% faster.
