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Univ.-Prof. Dr.-Ing. Horst-Michael Groß

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Multi Feature Fusion at Score-Level for Appearance-based Person Re-Identification


This MATLAB-Code provides algorithms for score-level fusion with application to person re-identification.

This includes eight methods for score normalization

    • Product of likelihood-ratios
    • Logistic regression
    • FAR-based
    • Min-max
    • Z-score
    • Decimal scaling
    • Doble sigmoid
    • Tanh-estimators

    and eight methods for feature weighting

    • Equal weighting
    • EER-based (equal error rate)
    • Based on Rank 1 performance
    • Based on Rank 10 performance
    • nAUC-based (normalized area under cumulative matching characteristic (CMC) curve)
    • D-prime
    • NCW (non-confidence width)
    • PROPER (pairwise optimization of projected genuine-impostor overlap)

    The framework provides interfaces for evaluation on the frequently used person re-identification benchmark datasets

    • VIPeR
    • iLIDS
    • ETHZ

    The framework also contains implementations of publicly available features for appearance-based person re-identification

    • Texture features: Local Binary Pattern (LBP), Maximum Response (MR8)
    • Color features: Black Value Tint (BVT) histogram, Lightness Color Opponent (Lab) histogram, weighted Hue Saturation Value (wHSV) histogram, Maximum Stable Color Regions (MSCR)
    • Combinations: Ensemble of Localized Features (ELF), Salient Dense Correspondence (SDC)

    Additionally, the framework provides interfaces to combine score-level fusion with feature-level fusion (metric learning with KISSME or kernel-LFDA).


    If you consider using the code provided on this page, please reference the following:

    Eisenbach, M., Kolarow, A., Vorndran, A., Niebling, J., Gross, H.-M.
    Evaluation of Multi Feature Fusion at Score-Level for Appearance-based Person Re-Identification
    Proc. of Int. Joint Conf. on Neural Networks (IJCNN 2015), Killarney, Ireland, pp. 469-476, IEEE 2015


    Please contact for requests.