Suspect identification based on descriptive facial attributes

We present a method for using human describable face attributes to perform face identification in criminal inves-tigations. To enable this approach, a set of 46 facial at-tributes 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 extrac-tion is performed using localized face components to im-prove 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 sketch-based face recognition by allowing investigators to imme-diately search face repositories without the time delay that is incurred due to sketch generation. 1

Linear inverse system approach in the context of chaotic communications

A chaotic signal masking scheme involving the inverse system approach which uses nonautonomous chaotic systems is presented. In this context, two simple second-order nonautonomous systems are studied to demonstrate how such forced systems can be used for private communication purposes. The two main difficulties arising in such applications are the chaos synchronization and the exact duplication of the transmitter's nonlinear parts at the receiving end also. It is shown that our approach overcomes such difficulties by making use of second-order forced systems. Examples of circuit implementations for the transmitter and the receiver subsystems are discussed to investigate different recovering techniques

Forecasting TV ratings of Turkish television series using a two-level machine learning framework

TV rating is a numeric estimate of the popularity of television programs. Forecasting TV ratings is considered an important asset for investment planning of media due to its potential of reducing the risks of future ventures. The aim of this study is to develop a machine learning model capable of efficiently forecasting the TV ratings of Turkish TV series in a practical manner. To this end, two prediction models were proposed for forecasting the TV ratings of television series, facilitating an extensive set of features. A contribution of this study is the inclusion of social media-based features using search trends around television series and exploration of the viability of using these features in place of temporal features. The study presents an extensive evaluation of the forecast performance of the proposed models. The performance of the proposed models were evaluated using a data collection composed of ratings and various attributes of series and their episodes aired at prime-time Turkish broadcast during 2014 and 2018. In the experiments, a theoretical forecast performance was first established with the inclusion of temporal features. Next, a set of practical models were proposed, replacing temporal attributes with social media-based attributes, relating to internet popularity and visibility of the series. The experiments show that, the proposed models achieve up to 1.65% error rate for the theoretical setting and 7.06% error rate for the practical setting

Towards automated caricature recognition

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