Demographic estimation entails automatic estimation of age, gender and race of a person from his face image, which has many potential applications ranging from forensics to social media. Automatic demographic estimation, particularly age estimation, remains a challenging problem because persons belonging to the same demographic group can be vastly different in their facial appearances due to intrinsic and extrinsic factors. In this paper, we present a generic framework for automatic demographic (age, gender and race) estimation. Given a face image, we first extract demographic informative features via a boosting algorithm, and then employ a hierarchical approach consisting of between-group classification, and within-group regression. Quality assessment is also developed to identify low-quality face images that are difficult to obtain reliable demographic estimates. Experimental results on a diverse set of face image databases, FG-NET (1K images), FERET (3K images), MORPH II (75K images), PCSO (100K images), and a subset of LFW (4K images), show that the proposed approach has superior performance compared to the state of the art. Finally, we use crowdsourcing to study the human perception ability of estimating demographics from face images. A side-by-side comparison of the demographic estimates from crowdsourced data and the proposed algorithm provides a number of insights into this challenging problem.
Carney triad (CT) is a rare condition with synchronous or metachronous occurrence of gastrointestinal stromal tumors (GISTs), paragangliomas (PGLs), and pulmonary chondromas in a patient. In contrast to Carney–Stratakis syndrome (CSS) and familial PGL syndromes, no germline or somatic mutations in the succinate dehydrogenase (SDH) complex subunits A, B, C, or D have been found in most tumors and/or patients with CT. Nonetheless, the tumors arising among patients with CT, CSS, or familial PGL share a similar morphology with loss of the SDHB subunit on the protein level. For the current study, we employed massive parallel bisulfite sequencing to evaluate DNA methylation patterns in CpG islands in proximity to the gene loci of all four SDH subunits. For the first time, we report on a recurrent aberrant dense DNA methylation at the gene locus of SDHC in tumors of patients with CT, which was not present in tumors of patients with CSS or PGL, or in sporadic GISTs with KIT mutations. This DNA methylation pattern was correlated to a reduced mRNA expression of SDHC, and concurrent loss of the SDHC subunit on the protein level. Collectively, these data suggest epigenetic inactivation of the SDHC gene locus with functional impairment of the SDH complex as a plausible alternate mechanism of tumorigenesis in CT.
Abstract-As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver's license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a "person of interest," often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, our work considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3D face models, and incorporation of quality measures for fusion and video frame selection.
Abstract-In this work, we attempt to address the following problem: Given a large number of unlabeled face images, cluster them into the individual identities present in this data. We consider this a relevant problem in different application scenarios ranging from social media to law enforcement. In large-scale scenarios the number of faces in the collection can be of the order of hundreds of million, while the number of clusters can range from a few thousand to millions-leading to difficulties in terms of both run-time complexity and evaluating clustering and per-cluster quality. An efficient and effective Rank-Order clustering algorithm is developed to achieve the desired scalability, and better clustering accuracy than other well-known algorithms such as k-means and spectral clustering. We cluster up to 123 million face images into over 10 million clusters, and analyze the results in terms of both external cluster quality measures (known face labels) and internal cluster quality measures (unknown face labels) and run-time. Our algorithm achieves an F-measure of 0.87 on a benchmark unconstrained face dataset (LFW, consisting of 13K faces), and 0.27 on the largest dataset considered (13K images in LFW, plus 123M distractor images). Additionally, we present preliminary work on video frame clustering (achieving 0.71 F-measure when clustering all frames in the benchmark YouTube Faces dataset). A per-cluster quality measure is developed which can be used to rank individual clusters and to automatically identify a subset of good quality clusters for manual exploration.
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