Facial identification from online images for use in the prevention of child trafficking and exploitation
Every year, an estimated 1.2 million children are trafficked (International Labour Office, 2002). The National Center for Missing & Exploited Children (NCMEC) received a 432% increase in child sexual abuse images for the purposes of identification between 2005 and 2009 (U.S. Department of Justice, 2010), and they assisted in the identification of 2,589 victims related to indecent images of children in 2015 (NCMEC, 2015a). In relation to the vast number of images received, machine-based facial recognition could help law enforcement and other organisations to match faces more efficiently. The use of facial recognition technology has become more popular within our society, but where rapid juvenile growth changes facial features recognition is challenging, especially for children under 15 years of age with changes to the outer contour of the face (Ramanathan and Chellappa, 2006). The challenge not only relates to the growth of the child’s face, but also relates to face recognition in the wild with unconstrained images. This study aims to provide an open-access database of facial images, documenting the different stages of facial growth from numerous individuals from birth to 19 years of age. There are currently very limited longitudinal databases available for the research community, and the collection of this database will benefit all researchers who wish to study age progression and facial growth. Ferguson (2015) suggested that facial recognition algorithms can perform better than humans in the identification of faces of children. Experiment 1 of this research takes a further step to explore how the difference in age group and age gap can affect the recognition rate using various facial recognition software, and explores the possibilities of group tagging. Results indicated that the use of multiple images is beneficial for the facial identification of children. Experiment 2 explores whether age progression work could further improve the recognition rate of juvenile faces. This study documents the workflow of a new method for digital manual age progression using a combination of previously published methods. The proposed age progression method for children recorded satisfactory levels of repeatability with facial measurements at the Nasion (n) and Trichion (tr) showing the most inaccuracy. No previous studies have tested how different conditions (i.e. blurring, resolution reduction, cropping and black and white) can affect machine-based facial recognition nor have they explored the relationship between age progression images and facial recognition software. The study found that reduction of the resolution of an age progression image improves automated facial recognition for juvenile identification, and manual age progressions are no more useful than the original image for facial identification of missing children. The outcome of this research directly benefits those who practice facial identification in relation to children, especially for age progression casework.
QA75 Electronic computers. Computer science, QA76 Computer software
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