Modeling Uncertainties in Performance of Object Recognition
Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated
random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. Proposed are the probability models for two kinds of data obtained
with two distinct goals of recognition: identification and discovery.
This research project builds novel statistical models for multi-biometric systems using geometric and multinomial distributions. These models are generic as they are only based on the
similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to
be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the
database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying
matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.
Prediction of Recognition Performance on Large Populations
We have addressed the estimation of a small gallery size that can generate the optimal error
estimate and its confidence on a large population (relative to the size of the gallery). It uses a
generalized two-dimensional prediction model that combines a hypergeometric probability distribution
model with a binomial model and also considers the data distortion problem in large populations.
Learning is incorporated in the prediction process in order to find the optimal small gallery size and
to improve the prediction. The Chernoff and Chebychev inequalities are used as a guide to obtain the
small gallery size. Results for the prediction are presented for the NIST-4 fingerprint
Prediction and Validation of Indexing Performance for Biometrics
The performance of a recognition system is usually experimentally
determined. Therefore, one cannot predict the
peiformance of a recognition system a priori for a new
dataset. In this research, a statistical model to predict the
value of k in the rank-k identification rate for a given biometric
system is presented. Thus, one needs to search only
the topmost k match scores to locate the true match object.
A geometrical probability distribution is used to model the
number of non match scores present in the set of similarity
scores. The model is tested in simulation and by using
a public dataset. The model is also indirectly validated
against the previously published results. The actual results
obtained using publicly available databases are very close
to the predicted results which validates the proposed model.
Predicting Fingerprint Biometrics Performance from a Small Gallery
We present a binomial model to predict both fingerprint verification and identification performance.
The match and non-match scores are computed, using the number of corresponding triangles as the match
metric, between the query and gallery fingerprints.
The match score and non-match score in a binomial prediction model are used to predict the performance on
large (relative to the size of the gallery) populations from a small gallery.
Performance Prediction for Individual Recognition by Gait
Existing gait recognition approaches do not give their theoretical or experimental performance predictions.
Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. In this
paper, we first propose a kinematic-based approach to recognize human by gait. The proposed approach
estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the
2D silhouette extracted from a monocular image sequence.
Predicting an Upper Bound on SAR ATR Performance
We present a method for predicting a tight upper bound on performance of a vote-based approach for
automatic target recognition (ATR) in synthetic aperture radar (SAR) images. The proposed method
considers data distortion factors such as uncertainty, occlusion,
and clutter, as well as model factors such as structural similarity. The proposed method is validated using
MSTAR public SAR data, which are obtained
under different depression angles, configurations, and articulations
Predicting Performance of Object Recognition
We present a method for predicting fundamental performance of object recognition.
The proposed method considers data distortion factors such as uncertainty,
occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider
only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between
every pair of model objects is captured. In the second stage, the similarity information is used along with statistical
models of the data-distortion factors to determine an upper bound on the probability of recognition error.
This bound is directly used to determine a lower bound on the probability of correct recognition.
Predicting Object Recognition Performance Under Data Uncertainty, Occlusion and Clutter
We present a novel method for predicting the performance
of an object recognition approach in the presence
of data uncertainty, occlusion and clutter. The
recognition approach uses a vote-based decision criterion,
which selects the object/pose hypothesis that has
the maximum number of consistent features (votes)
with the scene data. The prediction method determines
a fundamental, optimistic, limit on achievable
performance by any vote-based recognition system. It
captures the structural similarity between model objects,
which is a fundamental factor in determining
the recognition performance. Given a bound on data
uncertainty, we determine the structural similarity between
every pair of model objects. This is done by
computing the number of consistent features between
the two objects as a function of the relative transformation
between them. Similarity information is
then used, along with statistical models for data distortion,
to estimate the probability of correct recognition
(PCR) as a function of occlusion and clutter rates.
The method is validated by comparing predicted PCR
plots with ones that are obtained experimentally.