Abstract
Over the past two decades, researchers in the field of biometrics have presented a wide variety of coding-based palmprint recognition methods. These approaches mainly rely on extracting the texture features, e.g. line orientations, and phase information, using different filters. In this paper, we propose a new efficient palmprint recognition method based on the Different of Block Means. In the proposed scheme, only basic operations (i.e. mainly additions and subtractions) are used, thus involving a much lower computational cost when compared with existing systems. This makes the system suitable for online palmprint identification and verification. Furthermore, the technique has been shown to deliver superior performance over related systems.
Highlights
Since the early 2000s, the human palmprint has emerged as a robust means that can efficiently be used for verifying and/or identifying the personal identity of individuals
In [12], the authors attempted to enhance the robustness of coding-based palmprint identification techniques against small image transformations, e.g. shift and rotation by using a modified version of the Radon transform (FRAT), called the modified Radon transform (MFRAT), to generate a code-like matrix
Unlike existing coding-based palmprint identification techniques, this paper proposes a new, simple, and efficient palmprint coding technique based on the Difference of Block Means (DBM) that does not require any filtering operations
Summary
Since the early 2000s, the human palmprint has emerged as a robust means that can efficiently be used for verifying and/or identifying the personal identity of individuals. In [12], the authors attempted to enhance the robustness of coding-based palmprint identification techniques against small image transformations, e.g. shift and rotation by using a modified version of the Radon transform (FRAT), called the modified Radon transform (MFRAT), to generate a code-like matrix They presented a new matching method which takes into account small geometric changes by considering the neighbourhood of each pixel in the Radon-filtered image. The aforementioned coding-based techniques mainly rely on texture, contour, and edge features and are characterised by high identification accuracy and low computational complexity, making such systems suitable for real-time applications These features have been exploited on palm-vein images recently in biometric systems [18,19].
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