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Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

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Abstract

The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

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Authors and Affiliations

  1. LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria

    Abdelouahab Attia

  2. State University of New York Polytechnic Institute, Marcy, NY, USA

    Zahid Akhtar

  3. University of Caen, Caen, France

    Youssef Chahir

Authors
  1. Abdelouahab Attia
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  2. Zahid Akhtar
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  3. Youssef Chahir
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Correspondence to Abdelouahab Attia.

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Attia, A., Akhtar, Z. & Chahir, Y. Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication. SIViP 15, 851–859 (2021). https://doi.org/10.1007/s11760-020-01806-0

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