No abstract

Highly informative genetic markers, such as simple sequence repeats (SSRs), can be used to directly measure pollen flow by parentage analysis. However, mistyping (i.e. false inference of genotypes caused by the occurrence of null alleles, mutations, and detection errors) can lead to substantial biases in the estimates obtained. Using computer simulations, we evaluated a direct method for estimating pollen immigration using SSR markers and a paternity exclusion approach. This method accounts for mistyping and does not rely on assumptions about the distribution of male reproductive success. If ignored, even minor rates of mistyping (1.5%) resulted in overestimating pollen immigration by up to 150%. When we required at least two mismatching loci before excluding candidate fathers from paternity, the resulting pollen immigration estimates had small biases for rates of mistyping up to 4.5%. Requiring at least three mismatches for exclusion was needed to minimize the upward biases of pollen immigration caused by rates of mistyping up to 10.5%. The minimum number of highly variable SSR loci needed to minimize cryptic gene flow and obtain reliable estimates of pollen immigration varied from five to seven for a sampling scheme applicable to most conifers (i.e. when paternal haplotypes can be unambiguously determined). Between five and nine highly variable SSR loci were needed for a more general sampling scheme that is applicable to all diploid seed plants. With moderately variable SSR markers, consistently accurate estimates of pollen immigration could be obtained only for rates of mistyping up to 4.5%. We developed the POLLEN FLOW (PFL) computer program which can be used to obtain unbiased and precise estimates of pollen immigration under a wide range of conditions, including population sizes as large as 600 parents and mistyping rates as high as 10.5%.

Considerable progress has been made in face recognition research over the last decade especially with the development of powerful models of face appearance (i.e., eigenfaces). Despite the variety of approaches and tools studied, however, face recognition is not accurate or robust enough to be deployed in uncontrolled environments. Recently, a number of studies have shown that infrared (IR) imagery offers a promising alternative to visible imagery due to its relative insensitive to illumination changes. However, IR has other limitations including that it is opaque to glass. As a result, IR imagery is very sensitive to facial occlusion caused by eyeglasses. In this paper, we propose fusing IR with visible images, exploiting the relatively lower sensitivity of visible imagery to occlusions caused by eyeglasses. Two different fusion schemes have been investigated in this study: (1) imagebased fusion performed in the wavelet domain and, (2) feature-based fusion performed in the eigenspace domain. In both cases, we employ Genetic Algorithms (GAs) to find an optimum strategy to perform the fusion. To evaluate and compare the proposed fusion schemes, we have performed extensive recognition experiments using the Equinox face dataset and the popular method of eigenfaces. Our results show substantial improvements in recognition performance overall, suggesting that the idea of fusing IR with visible images for face recognition deserves further consideration.

Abstract-In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). Exhaustive matching against a large number of identities increases the response time of the system and may also reduce the accuracy of identification. One way to reduce the response time is by designing biometric templates that allow for rapid matching, as in the case of IrisCodes. An alternative approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

Models which describe road traffic patterns can be helpful in detection and/or prevention of uncommon and dangerous situations. Such models can be built by the use of motion detection algorithms applied to video data. Block matching is a standard technique for encoding motion in video compression algorithms. We explored the capabilities of the block matching algorithm when applied for object tracking. The goal of our experiments is twofold: (1) to explore the abilities of the block matching algorithm on low resolution and low frame rate video and (2) to improve the motion detection performance by the use of different search techniques during the process of block matching. Our experiments showed that the block matching algorithm yields good object tracking results and can be used with high success on low resolution and low frame rate video data. We observed that different searching methods have small effect on the final results. In addition, we proposed a technique based on frame history, which successfully overcame false motion caused by small camera movements.