Evaluation of the Pre-processing Methods in Image-Based Palmprint Biometrics

Wojciechowska, Agata; Choraś, Michał; Kozik, Rafał

doi:10.1007/978-3-319-68720-9_6

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…To preserve the spatial characteristics and to form a robust local feature descriptor, multi-region LBP pattern-based features [4] Local features from successive regions are concatenated to form a combining the two results in one vector with 512 features, the results in Tables 5 and 6.…”

Section: Resultsmentioning

confidence: 99%

“…Beside content-based image retrieval (CBIR), digital image processing plays a vital role in numerous areas such as processing and analyzing medical image [1], image inpainting [2], pattern recognition [3], biometrics [4], multimedia security [5], and information hiding [6]. In the area of image processing and computer vision, CBIR has grown increasingly as an advanced research topic.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Two-layer based Technique for Content-based Image Retrieval

Salih

Abdulla

2021

UHD J SCI TECH

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The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Two-layer based Technique for Content-based Image Retrieval

Salih

Abdulla

2021

UHD J SCI TECH

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“…e second is ROI extraction. Selection of the proper preprocessing method is meaningful and can strongly affect the whole verification system's accuracy, a fact that was investigated in our previous work [16]. e summary of first approaches to palmprint recognition was presented in the book [17].…”

Section: Related Workmentioning

confidence: 99%

“…Among others, there are the Hough transform used in [18], the Haar discrete wavelet transform implemented in [19], and the discrete cosine transform used in [20]. ere are also some local descriptors applied to feature extraction: local binary patterns [21], SURF and SIFT descriptors [22], and histogram of oriented diagrams used in our previous work [16]. Another popular method is based on statistical principal component analysis (PCA) implemented in [23,24], which was presented, for example, in [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

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Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Giełczyk

Choraś

Kozik

2019

Mobile Information Systems

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Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

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