A study of quantitative comparisons of photographs and video images based on landmark derived feature vectors

Kleinberg, K.F. and Siebert, J.P. (2012) A study of quantitative comparisons of photographs and video images based on landmark derived feature vectors. Forensic Science International, 219(1-3), pp. 248-258. (doi: 10.1016/j.forsciint.2012.01.014)

Full text not currently available from Enlighten.


An abundunce of surveillance cameras highlights the necessity of identifying individuals recorded. Images captured are often unintelligible and are unable to provide irrefutable identifications by sight, and therefore a more systematic method for identification is required to address this problem. An existing database of video and photograhic images was examined, which had previously been used in a psychological research project; material consisted of 80 video (Sample 1) and 119 photograhic (Sample 2) images, though taken with different cameras. A set of 38 anthropometric landmarks were placed by hand capturing 59 ratios of inter-landmark distances to conduct within sample and between sample comparisons using normalised correlation calculations; mean absolute value between ratios, Euclidean distance and Cosine θ distance between ratios. The statistics of the two samples were examined to determine which calculation best ascertained if there were any detectable correlation differences between faces that fall under the same conditions. A comparison of each face in Sample 1 was then compared against the database of faces in Sample 2. We present pilot results showing that the Cosine θ distance equation using Z-normalised values achieved the largest separation between True Positive and True Negative faces. Having applied the Cosine θ distance equation we were then able to determine that if a match value returned is greater than 0.7, it is likely that the best match will be a True Positive allowing a decrease of database images to be verified by a human. However, a much larger sample of images requires to be tested to verify these outcomes.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Siebert, Dr Paul
Authors: Kleinberg, K.F., and Siebert, J.P.
College/School:College of Science and Engineering > School of Computing Science
Research Group:Computer Vision & Graphics
Journal Name:Forensic Science International
Journal Abbr.:Forensic Sci Int.
Publisher:Elsevier Ireland Ltd.
ISSN (Online):1872-6283
Published Online:12 January 2012

University Staff: Request a correction | Enlighten Editors: Update this record