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Newborn swapping, missing, mixing, and illegal adoption is a global challenge and to resolve this emerging issue very less research has been done. Most of the biometric systems are developed for adults and extremely few of them address the difficulty of newborn recognition. As they are the highly non cooperative users of biometrics the ear of newborn may be a perfect source of data for passive identification...
This paper introduces the concept of positive developmental video classification. The work focuses on developing features and classification systems that can be used to classify content based on the impact on the cognitive, social and academic development of children according to an expertly assigned predefined positive or negative cognitive impact category. We solve the problem by developing novel...
Most of the biometric research being done is for adults and the identification accuracy of newborn are least reported in the literatures. In this paper we propose a novel biometric identification method for newborn babies using their face and soft biometrics. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable pediatric patients. The...
Newborn swapping and abduction is a global problem and traditional approaches such as ID bracelets and footprinting do not provide the required level of security. This paper introduces the concept of using face recognition for identifying newborns and presents an automatic face recognition algorithm. The proposed multiresolution algorithm extracts Speeded up robust features and local binary patterns...
This study want to find electrophysiological evidence to test whether face capture more attention than object, and whether the attention capture of face automatically or controllably. Fifteen subjects were tested on a three-stimulus visual oddball task, in which faces and objects as distractor stimuli were presented in different blocks separately. Face as task-irrelevant stimuli elicited earlier,...
Automatic facial image analysis has been a long standing research problem in computer vision. A key component in facial image analysis, largely conditioning the success of subsequent algorithms (e.g. facial expression recognition), is to define a vocabulary of possible dynamic facial events. To date, that vocabulary has come from the anatomically-based Facial Action Coding System (FACS) or more subjective...
We propose a novel method for extracting semantic information about a verb's arguments and apply it to verb sense disambiguation (VSD). We contrast this method with two popular approaches to retrieving the same kind of information and show that it improves the performance of our VSD system and outperforms the other two approaches.
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