Quantification of Facial Asymmetry for Expression-invariant Human Identification

1. Quantification of Facial Asymmetry for Expression-invariant Human Identification Yanxi Liu yanxi@cs.cmu.edu The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA USA

2. Acknowledgement • Joint work with Drs. Karen Schmidt and Jeff Cohn (Psychology, U. Of Pitt). • Students who work on the data as research projects: Sinjini Mitra, Nicoleta Serban, and Rhiannon Weaver (statistics, CMU), Yan Karklin, Dan Bohus (scomputer science) and Marc Fasnacht (physics). • Helpful discussions and advices provided by Drs. T. Minka, J. Schneider, B. Eddy, A. Moore and G. Gordon. • Partially funded by DARPA HID grant to CMU entitled: • “Space Time Biometrics for Human Identification in Video”

3. Human Faces are Asymmetrical Left Face Right Face

4. Under Balanced Frontal Lighting (from CMU PIE Database)

5. What is Facial Asymmetry? • Intrinsic facial asymmetry in individuals is determined by biological growth, injury, age, expression … • Extrinsic facial asymmetry is affected by viewing orientation, illuminations, shadows, highlights …

6. Extrinsic Facial asymmetry on an image is Pose-variant Left face Original Image Right Face

7. Facial Asymmetry Analysis • A lot of studies in Psychology has been done on the topics of • attractiveness v. facial asymmetry (Thornhill & Buelthoff 1999) • expression v. facial movement asymmetry • Identification • Humans are extremely sensitive to facial asymmetry • Facial attractiveness for men is inversely related to recognition accuracy (O’Toole 1998) Limitations: qualitative, subjective, still photos

8. Motivations • Facial (a)symmetry is a holistic structural feature that has not been explored quantitatively before • It is unknown whether intrinsic facial asymmetry is characteristic to human expressions or human identities

9. The question to be answered in this work How does intrinsic facial asymmetry affect human face identification?

10. DATA: Expression VideosCohn-Kanade AU-Coded Facial Expression Database Neutral Peak joy anger disgust

11. Sample Facial Expression Frames Total 55 subjects. Each subject has three distinct expression videos of varied number of frames. Total 3703 frames. Neutral Joy Disgust Anger

12. Face Midline Face Image Normalization Inner canthus Philtrum Affine Deformation based on 3 reference points

13. Quantification of Facial Asymmetry 1. Density Difference: D-face D (x,y) = I(x,y) – I’(x,y) I(x,y) --- normalized face image, I’(x,y) --- bilateral reflection of I(x,y) about face midline 2. Edge Orientation Similarity: S-face S(x,y) = cos(Ie(x,y),I’e(x,y)) where Ie, Ie’ are edge images of I and I’ respectively,  is the angle between the two gradient vectors at each pair of corresponding points

14. Asymmetry Faces An half of D-face or S-face contains all the needed information. We call these half faces Dh, Sh,Dhx, Dhy, Shx,ShyAsymFaces. Original D-face S-face

15. Asymmetry Measure Dhy for two subjects each has 3 distinct expressions Dhy Dhy forehead forehead chin chin Joy anger | disgust Joy | anger | disgust

16. spatial temporal Forehead -- chin Forehead -- chin Forehead -- chin

17. spatial temporal Forehead -- chin Forehead -- chin Forehead -- chin

18. spatial Forehead -- chin Forehead -- chin Forehead -- chin

19. Evaluation of Discriminative Power of Each Dimension in SymFace Dhy Variance Ratio Bridge of nose forehead chin

20. Most Discriminating Facial Regions Found

21. Experiment Setup 55 subjects, each has three expression video sequences (joy, anger, disgust). Total of 3703 frames. Human identification test is done on ---- Experiment #1: train on joy and anger, test on disgust; Experiment #2: train on joy and disgust, test on anger; Experiment #3: train on disgust and anger, test on joy; Experiment #4: train on neutral expression frames,test on peak Experiment #5: train on peak expression frames,test on neutral The above five experiments are carried out using (1) AsymFaces, (2) Fisherfaces, and (3) AsymFaces and FisherFaces together.

22. Sample Results: Combining Fisherfaces (FF) with AsymFaces (AF) (Liu et al 2002) Data set is composed of 55 subjects, each has three expression videos. There are 1218 joy frames, 1414 anger frames and 1071 disgust frames. Total number of frames is 3703.

23. All combinations of FF and AF features are tested and evaluated quantitatively

24. Complement Conventional Face Classifier 107 pairs of face images taken from Feret database. It is shown that asymmetry-signature’s discriminating power demonstrated (1) has a p value << 0.001 from chance (2) is independent from features used in conventional classifiers, decreases the error rate of a PCA classifier by 38% (15%  9.3%)

25. Quantified Facial Asymmetry used for Pose estimation

26. Summary • Quantification of facial asymmetry is computationally feasible. • The intrinsic facial asymmetry of specific regions captures individual differences that are robust to variations in facial expression • AsymFaces provides discriminating information that is complement to conventional face identification methods (FisherFaces)

27. Future Work • (1) construct multiple, more robust facial asymmetry measures that can capture intrinsic facial asymmetry under illumination and pose variationsusing PIE as well as publicly available facial data. • (2) develop computational models for studying how recognition rates is affected by facial asymmetry under gender, race, attractiveness, hyperspectral variations. • (3) study pose estimation using a combination of facial asymmetry with skewed symmetry.