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@article{WSEAS-Struc_2009,

title = {Histogram remapping as a preprocessing step for robust face recognition},

author = {Vitomir Štruc and Janez Žibert and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/histogramremappingasapreprocessingstepforrobustfacerecognition/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {WSEAS transactions on information science and applications},

volume = {6},

number = {3},

pages = {520-529},

abstract = {Image preprocessing techniques represent an essential part of a face recognition systems, which has a great impact on the performance and robustness of the recognition procedure. Amongst the number of techniques already presented in the literature, histogram equalization has emerged as the dominant preprocessing technique and is regularly used for the task of face recognition. With the property of increasing the global contrast of the facial image while simultaneously compensating for the illumination conditions present at the image acquisition stage, it represents a useful preprocessing step, which can ensure enhanced and more robust recognition performance. Even though, more elaborate normalization techniques, such as the multiscale retinex technique, isotropic and anisotropic smoothing, have been introduced to field of face recognition, they have been found to be more of a complement than a real substitute for histogram equalization. However, by closer examining the characteristics of histogram equalization, one can quickly discover that it represents only a specific case of a more general concept of histogram remapping techniques (which may have similar characteristics as histogram equalization does). While histogram equalization remapps the histogram of a given facial image to a uniform distribution, the target distribution could easily be replaced with an arbitrary one. As there is no theoretical justification of why the uniform distribution should be preferred to other target distributions, the question arises: how do other (non-uniform) target distributions influence the face recognition process and are they better suited for the recognition task. To tackle this issues, we present in this paper an empirical assessment of the concept of histogram remapping with the following target distributions: the uniform, the normal, the lognormal and the exponential distribution. We perform comparative experiments on the publicly available XM2VTS and YaleB databases and conclude that similar or even better recognition results that those ensured by histogram equalization can be achieved when other (non-uniform) target distribution are considered for the histogram remapping. This enhanced performance, however, comes at a price, as the nonuniform distributions rely on some parameters which have to be trained or selected appropriately to achieve the optimal performance.},

keywords = {biometrics, face recognition, histogram, histogram remapping, image processing, preprocessing},

pubstate = {published},

tppubtype = {article}

}

@article{EV_2009_palms,

title = {Gaussianization of image patches for efficient palmprint recognition},

author = {Vitomir Štruc and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/gaussianizationofimagepatchesforefficientpalmprintrecognition/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Electrotechnical Review},

volume = {76},

number = {5},

pages = {245-250},

abstract = {In this paper we present a comparison of the two dominant image preprocessing techniques for palmprint recognition, namely, histogram equalization and mean-variance normalization. We show that both techniques pursue a similar goal and that the difference in recognition efficiency stems from the fact that not all assumptions underlying the mean-variance normalization approach are always met. We present an alternative justification of why histogram equalization ensures enhanced verification performance, and, based on the findings, propose two novel preprocessing techniques: gaussianization of the palmprint images and gaussianization of image patches. We present comparative results obtained on the PolyU database and show that the patch-based normalization technique ensures stat-of-the-art recognition results with a simple feature extraction method and the nearest neighbor classifier.},

keywords = {biometrics, gaussianization, histogram remapping, palmprint recognition, palmprints, preprocessing},

pubstate = {published},

tppubtype = {article}

}