Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Prediction

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.

Performance Prediction

Image showing three different databases where each one contains a particular modality from all subjects. 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

An example of feature extraction process; thinned image. 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 database.

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.