A guided manual method for juvenile age progression using digital images

Predicting the possible age-related changes to a child’s face, age progression methods modify the shape, colour and texture of a facial image while retaining the identity of the individual. However, the techniques vary between different practitioners. This study combines different age progression techniques for juvenile subjects, various researches based on longitudinal radiographic data; physical anthropometric measurements of the head and face; and digital image measurements in pixels. Utilising 12 anthropometric measurements of the face, this study documents a new workflow for digital manual age progression. An inter-observer error study (n = 5) included the comparison of two age progressions of the same individual at different ages. The proposed age progression method recorded satisfactory levels of repeatability based on the 12 anthropometric measurements. Seven measurements achieved an error below 8.60%. Facial anthropometric measurements involving the nasion (n) and trichion (tr) showed the most inconsistency (14–34% difference between the practitioners). Overall, the horizontal measurements were more accurate than the vertical measurements. The age progression images were compared using a manual morphological method and machine-based face recognition. The confidence scores generated by the three different facial recognition APIs suggested the performance of any age progression not only varies between practitioners, but also between the Facial recognition systems. The suggested new workflow was able to guide the positioning of the facial features, but the process of age progression remains dependant on artistic interpretation

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

On the analysis of factors influencing the performance of facial age progression

Facial age progression is the process of synthesizing a face image at an older age based on images showing a person at a younger age. The ability to generate accurate age progressed face images is important for a number of forensic investigation tasks. In this paper we analyze the performance of a number of publicly available age progression applications, with respect to different parameters encountered in age progression including imaging conditions of input images, presence of occluding structures, age of input/target faces, and age progression range. Through the analysis and quantification of age progression accuracy in the presence of different conditions, we extract a number of conclusions that take the form of a set of guidelines related to factors that forensic artists and age progression researchers should focus their attention in order to produce improved age progression methodologies