title: Personal identification based on skin texture features from the forearm and multi-modal imaging.
abstract: OBJECTIVE
  We investigate the use of skin texture features from the inner forearm as a means for personal identification. The forearm offers a number of potential advantages in that it is a fairly accessible area, and, compared with other zones such as fingertips, is less exposed to the elements and more shielded from wear.
  
  
  METHODS
  We extract and combine skin textural features from two imaging devices (optical and capacitive) with the aim of discriminating between different individuals. Skin texture images from 43 subjects were acquired from three different body parts (back of the hand, forearm and palm); testing used the two sensors either separately or in combination.
  
  
  RESULTS
  Skin texture features from the forearm proved effective for discriminating between different individuals with overall recognition accuracy approaching 96%.
  
  
  CONCLUSIONS
  We found that skin texture features from the forearm are highly individual-specific and therefore suitable for personal identification. Interestingly, forearm skin texture features yielded significantly better accuracy compared to the skin of the back of the hand and of the palm of the same subjects.
authors.affiliation: Department of Engineering, Università degli Studi di Perugia, 06125 Perugia, Italy.|School of Electrical Engineering and Computer Science, Queen Mary, University of London, London E1 4NS, UK.
authors.forename: F
authors.initials: F
authors.lastname: Bianconi
authors.affiliation: School of Engineering, London South Bank University, London, SE1 0AA, UK.|Sinlen Beauty Clinic, Sidcup DA14 6NF, UK.
authors.forename: E
authors.initials: E
authors.lastname: Chirikhina
authors.affiliation: School of Electrical Engineering and Computer Science, Queen Mary, University of London, London E1 4NS, UK.
authors.forename: F
authors.initials: F
authors.lastname: Smeraldi
authors.affiliation: School of Engineering, London South Bank University, London, SE1 0AA, UK.
authors.forename: C
authors.initials: C
authors.lastname: Bontozoglou
authors.affiliation: School of Engineering, London South Bank University, London, SE1 0AA, UK.
authors.forename: P
authors.initials: P
authors.lastname: Xiao
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D005542:Forearm / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods* / Q000639:trends; D010363:Pattern Recognition, Automated / Q000379:methods*; D000075082:Proof of Concept Study; D011996:Records / Q000592:standards*; D012867:Skin / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D055815:Young Adult
keywords: image processing; personal identification; skin texture; texture analysis