Tytuł prezentacji 

Call for coopera+on: 

biometric template ageing 

Adam Czajka 

V‐ce Head, Biometric Laboratories, NASK 

Assistant Professor, Warsaw Univ. of Technology  

Warsaw, Poland 

www.BiometricLabs.pl, adam.czajka@nask.pl 

IBPC 2010, March 4, 2010 

NIST Gaithersburg, MD 
Adam Czajka, adam.czajka@nask.pl 

Template ageing 

• Source of age‐related changes 

• Natural processes of our body or behavior changes 

• Not adequately studied for most of the biometric modaliLes 

• Indirect due to diseases, injuries and surgeries 

• Ageing sources for selected modali+es [Lan+nis 2010] 

• Iris: cataract (cloudiness of the eye lens), increase of blood pressure 

within the eyeball (oPen associated with glaucoma) 

• Fingerprints: low skin elasLcity = problems with fingerprint capture; 

wear and tear 

• Face: bone movement and growth (childhood, puberty), skin 

deformaLons (old age) due to lowering the skin elasLcity  and 

wrinkles 

• Voice: loss of lungs elasLcity, vocal muscle atrophy, anatomical 

changes in larynx, epithelium thickening  
Template ageing 

• Typical solu+ons 

• Template frequent (and forced) update 

• SimulaLon of the ageing effects  

• Based on „age progression” methods 

• InvesLgated for face biometrics [Giraldi 2009, Ramanathan 2006]  

(used when searching for missing persons), voice biometrics [Gurbuz 

2000] and fingerprints [Ryu 2007]  

• No adequate aaenLon in the literature for other modaliLes 

• Usage of age invariant biometric features 

• Lack of ample data bases 

• Biometrics: relaLvely young technology 

• Technological problems: equipment and measurement protocols 

must be kept for long Lme periods 

• Availability of volunteers during the database collecLon 

• Sociological resistance (e.g. creaLon and usage of templates for 

children) 

Adam Czajka, adam.czajka@nask.pl 
Ageing databases 

• MORPH  

(Craniofacial Longitudinal Morphological Face Database)  

[Ricanek 2006, www.faceaginggroup.com] 

• Biometric type: face 

• Two sets 

• Album 1: scanned photographs; populaLon: 515 subjects; Lme intervals 

from a few months up to 29 years;  

• Album 2: digital images; populaLon: 4000 subjects; Lme intervals: 

several years 

• Metadata available: race, date of birth, date of acquisiLon, gender,  

eye coordinates 

• Seems to be the largest publicly available ageing database  

(yet for only one modality) 

• Dedicated for use with standard face recogniLon algorithms 

• Research (ready) and commercial (planned) use 

Adam Czajka, adam.czajka@nask.pl 
Adam Czajka, adam.czajka@nask.pl 

Ageing databases 

• FG‐NET [www.fgnet.rsunit.com] 

• Biometric type: face

• PopulaLon: 82, 6‐18 images per subject 

• Auxiliary data available 

• Preprocessing data:  

– Face localizaLon (68 landmarks) 

– Existance of occluions (beard, moustache, hat, glasses) 

– Face verLcal and horizontal pose 

• Demographic data: age, gender 

• Research use only 

FG‐NET database sample 
Adam Czajka, adam.czajka@nask.pl 

Ageing databases 

• KFRIA Ageing DB  

(Korea Fingerprint Recogni+on Interoperability Alliance) 

[Ryu 2007] 

• Biometric type: fingerprints 

• Time interval: 1 year 

• PopulaLon: 100 subjects, three sensors used 

• Demographic data available: age, gender, occupaLon 
Adam Czajka, adam.czajka@nask.pl 

Ageing databases 

• BioBase (www.BiometricLabs.pl) 

• MulLmodal set: iris, face, 

handwri]en on‐line signatures, 

fingerprints, hand (2D geometry) 

• Time interval: 7 years 

• Equipment and capture protocols 

kept the same 

• PopulaLon: 50 subjects 

• Extension (planned for mid of 

2010) co‐sponsored by the  

Polish Ministry of Science and 

Higher EducaLon 
Adam Czajka, adam.czajka@nask.pl 

Coopera+on aspects 

• Iden+fica+on/development of coopera+on rules 

• Partners and end users, recogniLon technology suppliers 

• Project financing/coordinaLon 

• Iden+fica+on of the exis+ng databases gathering biometric 

measurements a few years apart 

• Development of tes+ng methodology and tools 

• Analysis of the work already done (especially for face 

and fingerprints) 

• Analysis of the end users requirements 
References 

[Lan+nis 2010]  

LanLnis, A. (2010), „A survey of the effects of aging on biometric idenLty 

verificaLon,” Interna'onal Journal of Biometrics, Vol. 2, No. 1, pp. 34‐52 

[Giraldi 2009] 

Giraldi, G. A. , Thomaz, C. E., „StaLsLcal Learning Models for AutomaLc Age 

Progression”, LNCC ‐ Petrópolis/RJ, LNCC Report 17/2009 

[Ramanathan 2006]

Ramanathan N., Chellappa R., „Face verificaLon across age progression,” IEEE 

TransacLons on Image Processing, Vol. 15, no. 11, pp. 3349‐3361, 2006 

[Ricanek 2006] Karl Ricanek Jr and Tamirat Tesafaye, "MORPH: A Longitudinal 

Image Database of Normal Adult Age‐Progression," IEEE 7th InternaLonal 

Conference on AutomaLc Face and Gesture RecogniLon, Southampton, UK, 

April 2006, pp 341‐345 

[Gurbuz 2000] S. Gurbuz, J.N. Gowdy, and Z. and Tufekci, "Speech spectrogram 

based model adaptaLon for speaker idenLficaLon," in Southeastcon 2000. 

Proceedings of the IEEE, Apr 2000, pp. 110‐115 

[Ryu 2007] J. Ryu, J. Jang, H. Kim, „Analysis of Effect of Fingerprint Sample Quality in 

Template Ageing”, NIST Biometric Quality Workshop, November 7‐8, 2007 

Adam Czajka, adam.czajka@nask.pl