Modeling automated detection of children in images
During a child pornography investigation, law enforcement officers are required to manually sort through potentially hundreds of thousands of images found on seized digital media. This process is error prone, time-consuming, and places considerable strain on department resources as well as the investigators themselves.^ This thesis examines the use of automated facial feature detection to estimate the age of a human in an image. The ability to automate the detection between an adult and a child in a sexually explicit digital image will assist law enforcement agencies combating the production and trafficking of child pornography.^ The proposed method uses open source Haar classifiers to extract facial feature information from images, and predict a subjects age based on anthropometric computations. Classification, regression, and hybrid approaches to machine learning are used to train and test age-estimation models. Manually-plotted facial landmarks are used as ground truth labels to measure the accuracy of 1) automated feature detection of the eyes, nose and mouth, and 2) age-estimation based on the extracted features. Currently, this methodology has been implemented to distinguish between classes of children and adults with an accuracy of approximately 70%.^
Health Sciences, Radiology|Computer Science
"Modeling automated detection of children in images"
Dissertations and Master's Theses (Campus Access).