Palmprint recognition based on the line feature local tri‐directional patterns

Abstract

Recent researches have shown that the texture descriptor local tri‐directional patterns (LTriDP) performs well in many recognition tasks. However, LTriDP cannot effectively describe the structure of palm lines, which results in poor palmprint recognition. To overcome this issue, this work proposes a modified version of LTriDP, called line feature local tri‐directional patterns (LFLTriDP), which takes into account the texture features of the palmprint. First, since palmprints contain rich lines, the line features of palmprint images, including orientation and magnitude, are extracted. The line features are more robust to variations compared to the original grayscale values. Then, the directional features are encoded as tri‐directional patterns. The tri‐directional patterns reflect the direction changes in the local area. Finally, the LFLTriDP features are constructed by the tri‐directional patterns, orientation and magnitude features. The LFLTriDP features effectively describe the structure of palm lines. Considering that most palm lines are curved, we use the concavity as supplementary information. The concavity of each pixel is obtained using the Banana filter and all pixels are grouped into two categories. The LFLTriDP features are refined to generate two feature vectors by the concavity to enhance the discriminability. The matching scores of the two feature vectors are weighted differently in the matching stage to reduce intra‐class distance and increase inter‐class distance. Experiments on PolyU, PolyU Multi‐spectral, Tongji, CASIA and IITD palmprint databases show that LFLTriDP achieves promising recognition performance.

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

“…The LTriDP descriptor is an extended version of the LBP descriptor which has a relationship with the neighbourhood pixels in all directions [23]. In a specific radius, every center pixel has eight neighbourhood pixels, and in the next radius, there are sixteen neighbourhood pixels, and so on.…”

Palmprint recognition based on gating mechanism and adaptive feature fusion

“…To validate the effectiveness of the proposed method in palmprint recognition, several state-of-the-art recognition methods that were tested on the Tongji palmprint dataset and the Poly-U palmprint dataset benchmarks are compared. This includes the proposed traditional descriptor-based palmprint recognition methods (Fei et al, 2020c(Fei et al, , 2022Jia et al, 2020;Zhou et al, 2020;Kusban, 2021;Li et al, 2022;Zhao et al, 2022) and the deep learning-based palmprint recognition methods (Alrahawe et al, 2021;Jia et al, 2021;Jing et al, 2021;Fei et al, 2022). A brief description of these methods is given in Table 1.…”

Enhanced histogram of oriented lines for palmprint recognition

“…In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure [5,6] . For palmprint recognition, many effective methods have been proposed [7][8][9][10][11] . Among all kinds of methods, HOG [12] is a powerful descriptor that has been exploited for biometrics such as face, gait, and palmprint recognition in recent years.…”