Facial Expression Recogni1on Using Ac1ve Appearance

Transcription

1 Facial Expression Recogni1on Using Ac1ve Appearance Pedro Mar1ns Joana Sampaio Jorge Ba1sta Ins1tute of Systems and Robo1cs Dep. of Electrical Engineering and Computers University of Coimbra

2 Introduc)on Facial expression has more influence than simple audio informa1on Human Computer Interface (HCI) Video compression Recogni1on of 7 different expressions [Ekman and Friesen] said that people are born with the ability to generate and interpret only six facial expressions: happiness, sadness, surprise, anger, fear and disgust. Facial expression recogni1on in s1ll images

3 Agenda Ac1ve Apperance Models Shape Model Texture Model Combined Model Linear Discriminant Analysis Classifica1on using malahonobis distance Results

4 Ac)ve Appearance Models AAM is a sta1s1cal based segmenta1on method, where the variability of shape and texture is captured from a dataset Able to extract relevant face informa1on without background interference Describes facial characteris1cs in a reduced model

5 Shape Model Shape defined as Generalised Procrustes Analysis (GPA) Remove Loca1on, scale and rota1on effects Raw Data Aligned Data

6 Sta)s)cal Shape Model Applying a PCA: x is the synthesized is the mean shape Φs contains the highest covariance texture eigenvectors bs is a vector of shape parameters represen1ng the weights

7 Texture Model For m pixels sampled, the texture is represented by: Required warping each image to a common reference frame Delaunay Triangula1on Each pixel is mapped barycentric coordinates

8 Texture Mapping Examples

9 Sta)s)cal Texture Model Applying a LowMemory PCA: g is the synthesized texture is the mean texture Φg contains the highest covariance texture eigenvectors bg is a vector of texture parameters

10 Combined Model To remove correla1ons between bs and bg a third PCA is performed Building an AAM instance Shape Texture in the mean shape frame AAM instance

11 AAM Instance Examples

12 Facial Expression Recogni)on Disgust Neutral Happy Fear Sad Anger Surprise

13 AAM Retained Variance How much variance should be retained in AAM building process? Variance (%) Number of Combined EigenVectors % 99% 99.9%

14 Linear Discriminant Analysis Supervised method that maximizes the between- class variance as well that minimizes the within- class variance Classifica1on using malahanobis distance Between- class scaeer matrix Within- class scaeer matrix is the sample in class i mean of class i mean of all classes number of classes number of samples in class i

15 LDA Evalua)on Metric How many eigenvectors hold on LDA? k- means clustering on result of LDA Neut Happ Sad Surp Ang Fear Disg Neut N Happ 0 N Sad 0 0 N Surp N Ang N 0 0 Fear N 0 Disg N K- means clustering result for ideal LDA Discrimination quality given by: Histogram for best LDA modes on 250 trials

16 Experimental Results 7 Expressions Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 97% Overal Recogni1on Rate = 55% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 98% Overal Recogni1on Rate = 56.5% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 99% Overal Recogni1on Rate = 61.2% Results by a Leave- one- out cross- valida1on scheme

17 Experimental Results 7 Expressions Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 97% Overal Recogni1on Rate = 55% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 98% Overal Recogni1on Rate = 56.5% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 99% Overal Recogni1on Rate = 61.2% Results by a Leave- one- out cross- valida1on scheme

18 Neutral/Sad Image Correla)ons

19 Neutral/Sad Image Correla)ons

20 Neutral/Sad Image Correla)ons

21 Neutral/Sad Image Correla)ons

22 Experimental Results 7 Expressions Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 97% Overal Recogni1on Rate = 55% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 98% Overal Recogni1on Rate = 56.5% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 99% Overal Recogni1on Rate = 61.2%

23 Experimental Results 7 Expressions Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 97% Overal Recogni1on Rate = 55% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 98% Overal Recogni1on Rate = 56.5% Neut Happ Sad Surp Ang Fear Disg Neut Happ Sad Surp Ang Fear Disg Confusion Matrix 99% Overal Recogni1on Rate = 61.2%

24 Anger/Disgust Image Correla1ons

25 Anger/Disgust Image Correla1ons

26 Anger/Disgust Image Correla1ons

27 Anger/Disgust Image Correla1ons

28 Facial Expression Recogni)on Disgust Neutral Happy Fear Sad Anger Surprise Neuroscience studies have showned that, in order to percieve sad and anger expressions, an especific cogni1ve process is required [Killgore and Yurgelun- Todd]

29 Experimental Results 5 Expressions Neut Happ Surp Fear Disg Neut Happ Surp Fear Disg Neut Happ Surp Fear Disg Neut Happ Surp Fear Disg Confusion Matrix 97% Overal Recogni1on Rate = 73.3% Confusion Matrix 98% Overal Recogni1on Rate = 78.09% Neut Happ Surp Fear Disg Neut Happ Surp Fear Disg Confusion Matrix 99% Overal Recogni1on Rate = 70.47%

30 5 Expressions Classifica1on Video

31 Final Notes Standart AAM to describe faces in a compact way LDA Discrimina1on quality given by k- means Classifica1on by mahalanobis distance 7 Expressions: Best recogni1on rate=61.2% (with 99% AAM variance) 5 Expressions: Best recogni1on rate=78.4% (with 98% AAM variance) Future work: Classify expression in a video sequence using HMM