Facial Identification of Children
: A Test of Automated Facial Recognition and Manual Facial Comparison Techniques on Juvenile Face Images

  • Eilidh Louise Ferguson

    Student thesis: Doctoral ThesisDoctor of Philosophy


    The accurate identification of children from facial photographs could provide a great attribute in the fight against child sexual exploitation, and may also aid in the detection of missing juveniles where comparative material is available. The European Commission is actively pursuing a global alliance for the identification of the victims of child sexual abuse; a task which is considered to be of the utmost importance.

    Images of child sexual abuse are shared, copied, and distributed online and their origin can be difficult to trace. Current investigations attempting to identify the children within such images appear to focus on the determination of places or geographical regions depicted in these images, from which victims can subsequently be tracked down and identified. Cutting edge technology is also used to detect duplicate images in order to decrease the workload of human operators and dedicate more time to the identification of new victims. Present investigations do not appear to focus on facial information for victim identification.

    Methods of facial identification already exist for adult individuals, consisting of both automated facial recognition algorithms and manual facial comparison techniques carried out by human operators. Human operator image comparison is presently the only method considered accurate enough to verify a face identity.

    It is only recently that researchers involved in automated facial recognition have begun to concern themselves with identification spanning childhood. Methods focus on age simulation to match query images with the age of the target database, rather than discrimination of individual faces over age progression. As far as can be determined, this is the first attempt to assess the manual comparison of juvenile faces.

    This study aimed to create a database of children’s faces from which identification accuracy could be tested using both automated facial recognition and manual facial comparison methods, which already exist for the identification of adults. A state-of-the-art facial recognition algorithm was employed and manual facial comparison was based on current recommendations by the Facial Identification Scientific Working Group (FISWG). It was not known if methods based on adult faces could be successfully extrapolated to juvenile faces, particularly as facial identification is highly susceptible to errors when there is an age difference between images of an individual. In children, the face changes much more rapidly than adults over ageing, due to the rapid growth and development of the juvenile face.

    The results of this study are in agreement with comparisons of automated and human performance in the identification of adult faces. Overall the automated facial recognition algorithm superseded human ability for identification of juvenile faces, however human performance was higher for the most difficult face pairs. The average accuracy for human image comparison was 61%. There was no significant difference in juvenile identification between individuals with prior experience of adult facial comparison and those with no prior experience. For automated facial recognition a correct identification rate of 71% was achieved at a false acceptance rate of 9%.

    Despite using methods created for adult facial identification, the results of this study are promising, particularly as they are based on a set of images acquired under uncontrolled conditions, which is known to increase error rates. With further augmentation of the database and investigation into child-specific identification techniques, the ability to accurately identify children from facial images is certainly a future possibility.
    Date of Award2015
    Original languageEnglish
    SponsorsCentre for Applied Science and Technology (CAST)
    SupervisorCaroline M. Wilkinson (Supervisor), Sue Black (Supervisor) & Caroline Wilkinson (Supervisor)


    • Facial identification
    • Children
    • Sexual exploitation

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