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Palmprint identification, a subcategory of biometrics identification, has become a hot research area, and image enhancement is a key problem in offline palmprint identification. Since the physiological characteristics and image quality of palmprints are different from those of fingerprints, existing algorithms on fingerprint image enhancement cannot be directly applied in offline palmprint images. Taking into account the characteristics of palmprint images, an enhancement algorithm specific to offline palmprint images is proposed in this paper. We have performed a series of experiments and provide the enhanced palmprint images in the experiment section. Moreover, we evaluate our algorithm by comparing it with the method only using a low-pass filter to smooth the images under the criteria of GI value. Besides, the running time of each step is given to show the efficiency of the algorithm. The result shows that our algorithm is capable of attaining the objectives of offline palmprint enhancement efficiently.

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This paper proposes a series of novel palmprint feature processing approaches based on the skeleton image. The skeleton images could be obtained from different kinds of input images and image processing approaches. This paper extracts both of the basic geometry attributes and additional structure information from the skeleton images. It extracts both of the palmprint minutiae feature and the local ridge feature, builds the relationship among the feature, and constructs the raw and rough feature set. For obtaining the final feature set, deleting the spurious feature while retaining the true feature as many as possible, the feature postprocessing approach proposed by this paper purifies the rough feature set based on the statistical and structural information, combing the information of the minutiae attribute, structural relationship in the minutiae subsets, the local ridge and the local region. We use and improve the point pattern matching approach in our previous work. It is a multi-phases minutiae matching based on both of the local structure and global feature. The experimental results reveal that the proposed feature processing approaches are effective and efficient for the practical requirement.

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