In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.

Vehicle counting, time-of-travel analysis, and other traffic studies frequently require the classification and identification of vehicles in a roadway. Unfortunately, many current technologies for identifying vehicles, such as image-based methods that use cameras and machine vision, are not appropriate for studies that require low-power consumption and low cost. Additionally, privacy issues are becoming a larger concern with the increasing controversy surrounding the public collection of imagery. In this work we evaluate a multi-modal sensing approach to vehicle classification and identification using an ensemble of sensors measurements including electromagnetic emanations and acoustic signatures. A novel kernel regression method is also used for signal learning to classify and identify vehicles without the need of invasive images. Multi-mode sensing, as well as signal learning, is shown to significantly increase the classification rate of specific vehicle classes.

Traditional correlation filters for object detection are efficient and provide good localization, but require scalar valued image features and only perform well on objects with consistent appearance. Some newer filters work with feature spaces that introduce some invariance to small deformations, but more difficult detection problems require more than one filter. We introduce a method for jointly learning an ensemble of correlation filters that collectively capture as much variation in object appearance as possible. During training our filters adapt to the needs of the training data with no restrictions on size or scope. We demonstrate performance that exceeds the state of the art in several challenging experiments.

Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability-they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age from a single “wild” image, where pose, illumination, expression, face size, and face occlusions are not managed. Our method is able to reduce the effects of variations that already exist within in image. Using pose-specific projections, we map image features into a latent space that is pose-insensitive and age-discriminative. Age estimation is then performed using a multi-class SVM. We show that our approach outperforms other published results on the Images of Groups dataset (Gallagher and Chen, 2009), which is the only age-related dataset with a non-trivial number of off-axis “wild” face images. We also show results that are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset, and we experimentally demonstrate that our feature projections introduce insensitivity to pose.

We present an approach to multi-target tracking that has expressive potential beyond the capabilities of chain-shaped hidden Markov models, yet has significantly reduced complexity. Our framework, which we call tracking-by-selection, is similar to tracking-by-detection in that it separates the tasks of detection and tracking, but it shifts temporal reasoning from the tracking stage to the detection stage. The core feature of tracking-by-selection is that it reasons about path hypotheses that traverse the entire video instead of a chain of single-frame object hypotheses. A traditional chain-shaped tracking-by-detection model is only able to promote consistency between one frame and the next. In tracking-by-selection, path hypotheses exist across time, and encouraging long-term temporal consistency is as simple as rewarding path hypotheses with consistent image features. One additional advantage of tracking-by-selection is that it results in a dramatically simplified model that can be solved exactly. We adapt an existing tracking-by-detection model to the tracking-by-selection framework, and show improved performance on a challenging dataset.