Blue-emitting bismuth-doped lanthanum oxide (La₂O₃: Bi³⁺) with various concentrations of Bi was synthesized using the sol–gel combustion method and used for visualization of latent fingerprints (LFPs). An X-ray diffraction (XRD) study revealed the hexagonal structure of the phosphors and total incorporation of the bismuth in the La₂O₃ matrix. Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to study the morphology and the relative vibrations of the synthesized samples. Photoluminescence (PL) studies showed strong blue emission around 460 nm due to the ³P₁ → ¹S₀ transition. Clear bright-blue fingerprint images were obtained with the powder dusting method on various surfaces like aluminum, compact discs, glass, wood and marble. A first evaluation of these images indicated a clear visualization of all three levels of details and a very high contrast ranging from 0.41 on marble to 0.90 on aluminum. As a further step, we used an algorithm for extracting fingerprint minutiae with which we succeeded in detecting all three levels of fingerprint details and even the most difficult ones, like open and closed pores. According to these analyses, La₂O₃: Bi phosphor is demonstrated to be an effective blue fluorescent powder for excellent visualization of latent fingerprints.

Authentication through the palmprint is a field of biometrics. Palmprint-based personal verification has quickly entered the biometric family. It has become increasingly popular in the recent years due to its ease of acquisition, reliability and high user acceptance. In this paper, we present an authentication system based on the palmprint. We are particularly interested in the feature extraction step. Three feature extraction techniques based on the discrete wavelet transform, the Gabor filters and the co-occurrence matrix are evaluated. The support vector machine is used for the classification step. The results have been validated on the PolyU database related to 400 users. The best results have been achieved with the wavelet decomposition

The rapid expansion of digital imagery, combined with increasingly sophisticated editing tools, has made image forgery a widespread and critical concern in fields such as journalism, forensics, and social media. This study provides a comprehensive review of Copy-Move Forgery Detection (CMFD) methods, focusing on the latest advances in deep learning-based techniques. We analyze key real-world challenges, summarize the most relevant recent solutions, and highlight persistent limitations that hinder robustness, accuracy, and practical deployment. A comparative review and qualitative analysis of prominent deep learning architectures reported in the literature is conducted to examine their relative efficiency, resilience, and trade-offs under diverse forgery scenarios. Finally, the paper highlights future research directions, including the development of more adaptable and generalizable models, the design of comprehensive benchmark datasets, the pursuit of real-time detection frameworks, and the enhancement of interpretability and transparency in CMFD systems.