Khalid Saeed | Biometrics | Best Researcher Award

Published on by Petroleum Engineering

Best Researcher Award

Khalid Saeed
Bialystok University of Technology, Poland

Khalid Saeed
Affiliation Bialystok University of Technology
Country Poland
Scopus ID 35183998800
Documents 215
Citations 1,479
h-index 20
Subject Area Biometrics
Event Petroleum Engineering Awards

Khalid Saeed is a researcher affiliated with Bialystok University of Technology in Poland whose reported scholarly profile is associated with biometrics, machine learning, image processing, gait analysis, and biometric recognition systems. The supplied research record lists 215 documents, 1,479 citations, and an h-index of 20. These indicators provide a quantitative view of research activity and citation impact, while individual publications provide additional context regarding the subjects and methods represented in the research portfolio. [1]

Abstract

This academic recognition profile presents the research record of Khalid Saeed in the field of biometrics. The supplied bibliometric information identifies 215 documents, 1,479 citations, and an h-index of 20 in association with Scopus Author ID 35183998800. [1] The publication examples supplied for this profile demonstrate research activity involving biometric gait systems, machine learning, image processing, multimodal biometric recognition, data augmentation, and related computational methods. Recent publications include work on quaternion-based data augmentation for biometric gait systems and a trimodal machine-learning-based biometric system. [2] [3]

Keywords

Biometrics; biometric recognition; gait biometrics; machine learning; computer vision; image processing; data augmentation; multimodal biometrics; trimodal biometrics; pattern recognition; artificial intelligence; sensor data; biometric authentication.

Introduction

Biometric technologies use measurable physical or behavioural characteristics to support the recognition or authentication of individuals. Contemporary biometric research increasingly combines sensing technologies, machine-learning algorithms, signal processing, and computer vision to improve the acquisition and interpretation of biometric information. Within this broader field, gait, fingerprint, anatomical, and other biometric modalities can be examined individually or jointly according to the requirements of a recognition system.[3]

Research Profile

The available profile data place biometrics at the centre of the stated subject area. The publication information further indicates an interdisciplinary connection between biometrics, machine learning, computer vision, sensor processing, and pattern-recognition techniques. Such intersections are characteristic of modern biometric research, where recognition performance depends not only on the selected biometric trait but also on data quality, feature representation, model design, augmentation, and evaluation methodology.

Research Contributions

The supplied publication record indicates several research directions. One concerns biometric gait systems and the generation of additional training samples from sensor-derived information. The 2025 study on quaternion-based augmentation describes a method designed to model common sensor disturbances and evaluate their effect on gait-biometric classification. [2][3]

Publications

Selected publications and scholarly works supplied for this profile are listed below. The list is illustrative rather than a complete bibliography of the reported 215 documents.

  • Earthquake Prediction in Levant Region Using Proposed CNN. M. AlBakoor, Majida; K. Saeed, Khalid; A. Massouh, Akram; N. AlHabbal, Nour. Conference paper.
  • Preface. R. Chaki, Rituparna; A. Cortesi, Agostino; N. Chaki, Nabendu; K. Saeed, Khalid. Editorial.
  • A New Image Thinning Algorithm. P. Milewski, Patryk; K. Saeed, Khalid. Conference paper.
  • Gait-Based Biometric Systems Integrating Augmented and Synthetic Samples. A. Sawicki, Aleksander; K. Saeed, Khalid. Conference paper, open access.
  • Using Machine Learning Techniques to Classify Fetal Congenital Malformations Early. M. AlBakoor, Majida; K. Saeed, Khalid; A. Massouh, Akram; et al. Conference paper.

Research Impact

The supplied bibliometric profile reports 1,479 citations and an h-index of 20 across 215 documents. [1] These metrics indicate a substantial body of indexed scholarly output and citation activity, although bibliometric indicators should be interpreted alongside publication quality, research contribution, authorship roles, field-specific citation practices, and the substantive significance of individual studies.

Award Suitability

Based on the information supplied for this profile, Khalid Saeed presents a research record that is relevant to consideration for a Best Researcher Award in a biometrics-oriented academic context. The reported volume of 215 documents, 1,479 citations, and an h-index of 20 provides quantitative evidence of sustained scholarly activity. [1]

Conclusion

Khalid Saeed’s supplied academic profile describes an established research record in biometrics and associated computational disciplines. The reported bibliometric indicators, together with selected publications on gait biometrics, biometric data augmentation, machine learning, and multimodal recognition, provide a basis for evaluating his scholarly activity. [1] [2] [3]

References

  1. Elsevier. (n.d.). Scopus author details: Khalid Saeed, Author ID 35183998800. Scopus.
    https://www.scopus.com/pages/authors/35183998800
  2. Sawicki, A., & Saeed, K. (2025). A quaternion-based augmenting method dedicated to biometric gait systems. International Journal of Applied Mathematics and Computer Science, 35(4), 631–649.
    DOI: https://doi.org/10.61822/amcs-2025-0045
  3. Szymkowski, M., & Saeed, K. (2025). Trimodal machine learning based biometrics system. Scientific Reports, 15, Article 20949.
    DOI: https://doi.org/10.1038/s41598-025-06288-z