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Aging-invariant Face Recognition. Department of Computer Science & Engineering College of Engineering. Unsang Park, Yiying Tong and Anil K. Jain. Introduction Facial aging is one of the many variations that degrades face recognition performance.

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Aging-invariant Face Recognition

Department of Computer Science & EngineeringCollege of Engineering

Unsang Park, Yiying Tong and Anil K. Jain

  • Introduction
  • Facial aging is one of the many variations that degrades face recognition performance.
  • We propose an automatic aging simulation technique that can assist any existing face recognition engine for age-invariant face recognition.
  • We learn the aging patterns in 3D domain by adapting a 3D morphable model to a 2D aging database (public domain FG-NET). The synthetic aging simulation process is applied on probe, gallery or both the images to compensate for the age-induced variation.
  • Aging pattern space construction
  • Aging pattern space comprises M x N 3D shapes, , where M (=70, from 0 to 69) is the number of different ages and N (=82) is the number of subjects.
  • Scale overall size of , so that the facial width at age i becomes consistent with the anthropometric data (modeling global aging pattern).
  • Fill missing data using interpolation: only 18% of the data in the M x N aging pattern space are originally available.
  • Face recognition with aging simulation
  • Let and be the probe and gallery, then aging simulation is applied on P, G or both as
    • , when the age difference is known (e.g., missing child or some screening scenarios).
    • , when the age difference is not known (e.g., most of the screening or multiple enrollment detection scenarios).
  • Experimental results
  • Face recognition performance is evaluated on original, pose corrected and aging-simulated images. Probe and gallery images are randomly selected 10 times with age gaps of 10, 20 and 30 and the average performance is reported.
  • Aging simulation
  • Objective: given a previously unseen shape Sx at age x, synthesize its aged/de-aged shape Sy at age y.
  • Method: Given Sx,new, estimate the weight w, that provides the best reconstruction of Sx as
  • where is the mean shape at age x. Then, the synthetic shape Sy at age y can be estimated using w and the shape space at age y as
  • Aging database
  • FG-NET database provides face images at different ages for 82 subjects (~12 images/subject) along with 68 feature points.
  • Original images also contain variations due to pose, lighting, expression and occlusion in addition to the aging.

Age 2

Age 5

Age 8

Age 10

Age 14

Age 16

Age 19

Age 22

Age 28

Age 33

Age 40

Fig. 1. Example face images of a subject across different ages in the FG-NET database

  • 3D model fitting
  • The 3D morphable model is adapted for the pose correction. The morphable model is composed of a mean shape, , and m orthogonal shape basis, si. Given a weight vector , a new 3D shape S can be generated as
  • The morphable model is fitted to the 2D shape S2d in terms of (R, T and) to minimize
  • where a is the scaling factor, P is the orthogonal projection matrix, R and T are the rotation and translation matrix, respectively. Then, the resulting 3D model, S3d, provides frontal view of the input 2D shape.
  • For each 2D shape, , for a subject i at age j, generate a corresponding 3D shape, .

Fig. 5. Cumulative Matching Characteristic (CMC) curve.

sub 1

sub 2

sub 3

sub N

age 0


age 1


at age x, estimate w



Age = x

Simulated Reconstruction

at age y, using w


age M-1


M x N aging pattern space

Fig. 3. Schematic of aging pattern space and aging simulation process.



(a) Probe (b) Probe (pose cor.) (c) Probe (aging sim.) (d) Gallery (pose cor.) (e) Gallery (f) aging simulation

Age 0 2 4 6 8 10

Fig. 6. Example face matching results for 7 subjects, one per row. Aging simulation gave correct matching at rank-1 for the first four subjects but not for the last two subjects.

  • Conclusions & Future work
  • A complete aging modeling framework in 3D domain using existing 2D database is proposed to develop an aging-invariant face recognition system.
  • Use of 3D aging modeling improves the rank-1 matching accuracy on FG-NET database from 12.6% up to 18.0% in various scenarios.
  • Future work will include incorporating texture modeling, face image quality based aging space construction and simulation, age estimation and the development of fully automatic aging correction system.

Input image at age=0

12 14 16 18 20

(a) Input image and its aging-simulated images from age 0 to 20

non-frontal2D shape

Input Image

3D Morphable Model

(b) Face images at five different poses from the aging-simulated image at age 20

Pose-corrected Texture

3D model in frontal view

Fig. 4. Example aging simulation and the multi-view capability of the simulated 3D aging model.

04. 18. 2008

Fig. 2. 3D model fitting process.