Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Aiadi, Oussama; Khaldi, Belal; Korichi, Aicha; Chaa, Mourad; Bezziane, Mohamed Ben; Omara, Ibrahim

doi:10.1007/s10586-024-04352-3

Cluster Comput

2024

DOI: 10.1007/s10586-024-04352-3

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Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Oussama Aiadi,

Belal Khaldi,

Aicha Korichi

et al.

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Cited by 2 publications

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From Geometry to Deep Learning: An Overview of Finger Knuckle Biometrics Recognition Approaches

Sumalatha,

Prakasha,

Prabhu

et al. 2024

IEEE Access

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From Geometry to Deep Learning: An Overview of Finger Knuckle Biometrics Recognition Approaches

Sumalatha,

Prakasha,

Prabhu

et al. 2024

IEEE Access

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A DWT-Based Approach with Gradient Analysis for Robust and Blind Medical Image Watermarking

Hebbache,

Khaldi,

Aiadi

et al. 2024

Applied Sciences

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The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

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Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Cited by 2 publications

References 34 publications

From Geometry to Deep Learning: An Overview of Finger Knuckle Biometrics Recognition Approaches

From Geometry to Deep Learning: An Overview of Finger Knuckle Biometrics Recognition Approaches

A DWT-Based Approach with Gradient Analysis for Robust and Blind Medical Image Watermarking

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