Single-sensor hand and footprint-based multimodal biometric ...

4 Preprocessing
4.1 Overview
The following information is provided by the preprocessing module in both hand- and
footprint-based systems:
• Normalised hand/foot image: this image contains the measured body part with
the following normalisation: (a) segmented from background, (b) aligned in direction
and position and (c) with removed arm and leg parts.
• Contour information: in order to identify fingers/toes within input images, preprocessing
comes along with hand/foot contour information including landmark indices,
such as salient points (i.e. intra-finger/intra-toe valleys and finger/toe tips).
This definition implies that each feature extractor is effectively provided with the same
information of the whole hand and may discard unused information within a featureextractor-dependent
processing step, before actual extraction takes place. In the strict
multibiometric sense of this definition, the employed scenario corresponds to multiple
matcher [14], whereas a slight modification of the preprocessor providing each modality
(fingerprint, palmprint, hand geometry) with already cropped input would classify the
scenario as multiple biometrics or multiple units (in case of fingerprints when each fingertip
is processed independently by the same matcher).
Hand- and footprint-based preprocessing consists of three consecutive steps:
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1. Segmentation: as introduced before, hand and foot only cover parts of the sensor
image. Thus, a coarse segmentation, by means of detecting the bounding box of
the actual handprint/footprint, is applied to increase computation speed of more
complex feature extraction. Segmentation typically also includes binarisation for
masking the background parts in the resulting image using either Otsu’s Thresholding
[29] (for hand images) or Canny edge detection [2] and thresholding (for
footprints).
2. Rotational alignment: when using peg-free systems like [43, 18], rotational alignment
has yet to be achieved. A frequently applied method is rigid hand registration
using statistical moments estimating the best-fitting ellipse, a technique from facedetection
[36]. While [43] also proposes finger-alignment for binary images, this
normalisation is not implemented in the proposed system due to textural distortions
caused by different spreadings of fingers. In contrast to more cooperative capture
(like in [18]), problems were experienced using ellipse fitting for hands with spread
fingers, thus a more elaborate approach was introduced using the palm borderline,
the least-squares approximated line between wrist and little finger.
3. Contour extraction and salient point detection: For hand-images, intra-finger
valleys (and peaks) can be detected as minima (and maxima) of the radial distance
function using a reference point at the wrist [43]. In my experiments, this method
is found to be fragile with respect to bad background segmentation, rings (causing
contour artifacts) and high resolutions. In this work a new method is proposed,
improving intra-finger valley candidates (provided by the radial distance function)