Clothing variations bring many
difficulties to gait recognition. We proposed a part-based approach to conquer the
problems brought by clothing variations. We partition a human body into several
parts, including overlapping parts. The overlapping is important because
clothing variations can be approximated by combinations of different parts. The
key ideas in this paper are listed as follows.
Part-based clothing categorization
We established a
clothing categorization to group similar clothes. In the training phase,
Probability Distribution Functions (PDFs) between
gait features are stored separately for each part and for both the same and
different clothes, and the corresponding discrimination capabilities are also
measured in advance. In the test phase, given a probe, posteriors with the same
and different clothes, are calculated based on the distances between the probe
and the galleries and on the trained PDFs.
Adaptive weight control
In a part-based
method, the main issue is how to combine the individual distances into a single
distance that quantifies the overall matching measure between a probe and the
gallery. We adopt the weighted sum of distances corresponding to each part as
the overall matching measure and adaptively control the weights for better
identification, by increasing them for the same part of clothing and vice versa.
Matching measures
We define 4 alternative
options for the matching measure. The first option is the mean value of the
minimum distances of the subsequences for each probe and gallery. The second is
the median value of the minimum distances of the subsequences for each probe
and gallery. The third is the minimum value of the minimum distances of the
subsequences for each probe and gallery. We propose a new technique using the
average of the 2 minimum distances of the subsequences for each probe and gallery.
The experimental results show that proposed measure gives the best results. We
have therefore chosen the average of the 2 minimum values, as our matching measure.