An effective biometric identification system using enhanced palm texture features: Adjustable local binary pattern: a new scheme for images texture analysis - Saigaa, Mohammed; Chitroub, Salim; Meraoumia, Abdallah (Evolving systems, 2022)
Pattern recognition technologies aim to develop new techniques for transforming original data into other representations that honestly illustrate their content and can then be used to analyze and understand the original data. In the field of computer vision based on images, which is the dominant technology for machine vision solutions, image texture is a rich information source, used in many feature extraction techniques to obtain salient and distinctive features that were later used in pattern recognition applications. However, depending on the application, the extracted feature vector may be subject to some constraints such as length and accuracy. This work aims to develop a new texture-based feature extraction technique that can provide a feature vector with a high degree of distinction that can be controlled in terms of length and accuracy. The new scheme, called adjustable local binary pattern (A-LBP) or adjacent block features based LBP, derived from the original LBP, uses neighboring blocks and a linear relationship between the features of these blocks to form a binary codeword’s that can be used to represent the image. In our study, the block feature is extracted using the discrete cosine transform. To evaluate the performance of the proposed scheme, an A-LBP based biometric and bio-watermarking systems were developed.