this report, I have tried to giveanoverview of the solutions found to date andtopropose a matcher that makes use of the structure of a piece to match. When I started the researchthatled to this report, thegoalwasto come up withaworking CLOS program thatcouldbeintegrated in POCO, a system that can

We address the problem of face verification using linear discriminant analysis and investigate the issue of matching score . We establish the reason behind the success of the normalised correlation. The improved understanding about the role of metric then naturally leads to a novel way

match score data

that intuitive solution of using solely best matching score is frequently suboptimal. Paper presents reasons for such behavior, and draws parallels with score normalization technique used in speaker identification. Two examples of real life applications illustrate the possible benefits of properly combining

We address the problem of face verification using linear discriminant anal-ysis and investigate the issue of matching score1. We establish the reason behind the success of the normalised correlation. The improved understand-ing about the role of metric then naturally leads to a novel way

units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of propensity score matching methods, and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units

Biometric applications can be divided into two types: verification tasks and identification tasks. For verification tasks a single matching score is given, and application accepts or rejects a matching attempt by thresholding the matching score. Based on the threshold value a performance

Position-specific scoring matrices are useful for representing and searching for protein sequence motifs. A sequence family can often be described by a group of one or more motifs, and an effective search must combine the scores for matching a sequence to each of the motifs in the group. We

it is necessary to identify and explore the possible ways by which reconstruction of template is feasible. In this paper, we show that even match scores carries sufficient information for reconstruction of the original face templates. We propose a novel non-iterative scheme to reconstruct face templates from

. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all