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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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

• The study assessed the accuracy of nasal morphology prediction of Rynn method. • Rynn method may not be representative and applicable of this sample. • Recalibration of the Rynn method and its formulae gave satisfactory results with less error. • Rynn method is easy to apply and practical for facial reconstruction practitioners. Prediction of the nose from the skull remains an important issue in forensic facial approximation. In 2010, Rynn et al. published a method of predicting nose projection from the skull. With this method, three craniometric measurements (x, y, z) are taken, and these are then used in regression formulae to estimate the nasal dimensions. The purpose of this study was to examine and test the accuracy of the Rynn et al. method and if necessary to adapt the formulae for this population. A sample of 90 CT scans of Turkish adults was used in the study. The actual and predicted dimensions were compared using t -test. The age of the individuals ranged from 20 to 40 years by sex. The descriptive statistics and correlations were calculated, and the actual and predicted measurements were compared. The differences between the actual and predicted values were statistically significant (p < 0.01), with −1 mm for males and −1.5 mm for females. Validation accuracies ranged from 76 to 92% in females and 72 to 82% in males. Recalibration equation accuracies ranged from 88 to 100% in females and 90 to 100% in males. The results showed that the recalibration of the Rynn et al. method and its formulae gave satisfactory results with less error and can be employed in facial approximation cases. [ABSTRACT FROM AUTHOR]