This paper addresses the problem of identifying a subject from a caricature. A caricature is a facial sketch of a subject's face that exaggerates identifiable facial features beyond realism, while still conveying his identity. To enable this task, we propose a set of qualitative facial features that encodes the appearance of both caricatures and photographs. We utilized crowdsourcing, through Amazon's Mechanical Turk service, to assist in the labeling of the qualitative features. Using these features, we combine logistic regression, multiple kernel learning, and support vector machines to generate a similarity score between a caricature and a facial photograph. Experiments are conducted on a dataset of 196 pairs of caricatures and photographs, which we have made publicly available. Through the development of novel feature representations and matching algorithms, this research seeks to help leverage the ability of humans to recognize caricatures to improve automatic face recognition methods.

[1] Physical and chemical properties of warm and hot spring waters as well as soil radon concentrations were measured continuously during a 3-year period in the Marmara region; following the devastating I : zmit earthquake of 17 August 1999 (Mw = 7.4). Promising and encouraging anomalies in ground radon emanation have been recorded and found to be closely related to seismic activity. The temporal and spatial variations in the soil radon data are presented. The earthquakes with magnitude >4 in the region were correlated with positive radon anomalies. Furthermore, during quiescence (absence of seismic activity) the radon data indicate random walk behavior of radon in soil and show Rayleigh-type probability density function (pdf), however, during the earthquake build-up period, the data show deviations from Rayleigh-type pdf. The radon positive anomalies indicate disturbance of the path of gas movement or gas release pattern prior to earthquakes. However, systematic and consistent anomalies in physical and/or chemical properties of the spring waters have not been detected for earthquakes occurring in the observation period (M < 5.3).

We present a method for using human describable face attributes to perform face identification in criminal investigations. To enable this approach, a set of 46 facial attributes were carefully defined with the goal of capturing all describable and persistent facial features. Using crowd sourced labor, a large corpus of face images were manually annotated with the proposed attributes. In turn, we train an automated attribute extraction algorithm to encode target repositories with the attribute information. Attribute extraction is performed using localized face components to improve the extraction accuracy. Experiments are conducted to compare the use of attribute feature information, derived from crowd workers, to face sketch information, drawn by expert artists. In addition to removing the dependence on expert artists, the proposed method complements sketchbased face recognition by allowing investigators to immediately search face repositories without the time delay that is incurred due to sketch generation.

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