DA Progress report 2 Multi-view facial expression. classification Nikolas Hesse
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1 DA Progress report 2 Multi-view facial expression classification Nikolas Hesse
2 Motivation Facial expressions (FE) play an important role in interpersonal communication FE recognition can help to improve natural humanmachine-interaction FE recognition systems exist, but they mainly concentrate on frontal faces To use FE recognition in real world situations, it is necessary to have systems that work for faces with pose variations as well Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 2
3 Outline Related work Multi-view facial expression classification using AAMs Goals Active Appearance Models Features Results Conclusion Future Work Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 3
4 Related Work Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 4
5 Related Work Hu et. al 2008 Multi-View Facial Expression Recognition Expression classification using different appearance features which were extracted from facial key-points and processed by different feature selection methods One classifier to detect the view, five emotion classifiers (one for each view) Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 5
6 BU-3DFE Database Data is taken from the Binghamton University 3D Facial Expression Database (BU-3DFE) Contains 3d models (with texture and annotated facial landmarks (83 landmarks per face)) Includes 100 subjects: 56 female, 44 male, age from 18 to 70 years, different ethnicities Each subject shows 7 expressions (anger, disgust, fear, happiness, neutral, sadness, surprise) with 4 levels of intensity (except neutral) 2,500 3D facial expression models Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 6
7 BU-3DFE Database - Examples Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 7
8 Related Work Data Images and shapes saved from 5 views Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 8
9 Related Work Features Features: Appearance descriptors, extracted at facial key points Feature Extraction: Local binary patterns (LBP) Histogram of oriented gradients (HoG) Scale-invariant feature transform (SIFT) Feature Selection: Locality preserving projection (LPP) Principal component analysis (PCA) Linear discriminant analysis (LDA) Classification: Nearest neighbor Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 9
10 Related Work Results Error Rates Raw appearance intensity: Original LPP PCA LDA 0 54,58 56,23 54,67 57, ,98 61,98 54,88 62, ,37 65,45 0,56 71, ,48 69,62 58,5 69, ,43 77,07 61,75 78,35 Average 57,16 66,07 57,18 67,91 Original LPP PCA LDA 0 50,13 35,67 50,48 46, ,32 34,88 51,5 45, ,77 36,33 52,74 59, ,5 34,82 51,56 49, ,84 37,12 54,95 60,59 Average 52,11 35,76 52,25 52,2 LBP: Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 10
11 Related Work Results Error Rates HoG: Original LPP PCA LDA 0 53,02 32,08 52,9 41, ,73 31,55 53,63 40, ,73 32,34 55,99 38, ,07 31,98 55,75 37, ,68 35,14 54,68 36,6 Average 54,65 32,62 54,59 38,79 Original LPP PCA LDA 0 43,68 27,24 43,93 40, ,19 26,13 43,27 40, ,54 26,65 44,23 55, ,64 26,14 44,79 42, ,84 28,55 43,89 48,1 Average 43,98 26,94 44,02 45,36 SIFT: Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 11
12 Multi-view facial expression recognition using AAMs Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 12
13 Goals Multi-view Facial expression recognition using Active Appearance Models (AAMs) Pose dependent AAMs, 1 AAM per pose (-90 to +90 degrees, 15 degree steps 13 models) Pose dependent SVM 1 SVM per pose (1vs1 multiclass classification) Expression classes: Anger, disgust, fear, happiness, sadness, surprise(, neutral) Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 13
14 Program overview (Fitted/aligned face images shown here do not correspond to shown input image) Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 14
15 Active Appearance Models Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 15
16 Active Appearance Models - Shape Shape: defined by mesh and vertex locations of mesh Linear shape variation: base shape s0 + linear combination of n shape vectors si; pi: shape parameters s = s0 + ni=1 pisi Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 16
17 Active Appearance Models - Appearance Appearance: image A(x) defined over pixels x ϵ s0 Appearance: base appearance A0(x) + linear combination of m appearance images Ai(x), λi appearance parameters A(x) = A0(x) + mi=1 λiai(x) Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 17
18 Active Appearance Models AAMs are used for matching a statistical model of object shape and appearance to a new image AAM model instance M(W(x;p)): computed by warping the appearance A from s0 to s using W(x;p) Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 18
19 Features Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 19
20 Features Shape coordinates Shape parameters Texture parameters SIFT descriptors Combination of features Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 20
21 SIFT Scale Invariant Feature Transform Get 'interest-points' from fitted AAM shape: frontal poses 68 points, side poses 36 points Extract SIFT-descriptor for each interest-point (128 values per interest-point) frontal: 8704 values, side: 4608 values Feature Selection (LDA, afterwards: 6 values) Train SVM using data feature vector LDA Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 21 smaller feature vector SVM Training
22 Classification Results Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 22
23 Classification accuracy average over all poses Features used C l a s s e s Raw shape coordinates Raw shape parameters Raw texture Raw shape parameters coordinates + texture parameters Raw shape LDA shape LDA SIFTparameters parameters + Features + texture LDA texture (without 90 parameters parameters left & right*) LDA shape parameters + LDA texture parameters + LDA SIFTFeatures Anger 55,3 44,7 61, ,3 55,9 58,4 63,8 Disgust 61,2 54,5 61,2 67,8 68,6 66,1 67,3 65,5 Fear 29 28,9 33, ,2 33,7 37,3 33,5 Happiness 72,6 61,4 65,5 73,1 68,1 66,4 69,9 69,6 Sadness 49,4 49,8 60,2 63,7 63,8 59,6 50,1 61,6 Surprise 69, ,7 76, , ,2 Neutral 9,4 9,1 26, ,4 21,2 12,8 12,9 *something wrong with those Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 23
24 Accuracy average over all emotions Features used A n g l e s Raw shape coordinates Raw shape parameters Raw texture Raw shape parameters coordinates + texture parameters Raw shape LDA shape LDA SIFT- LDA shape parameters parameters + Features parameters + + texture LDA texture LDA texture parameters parameters parameters + LDA SIFTFeatures 90l 49,7 48,5 54,2 58,2 58,9 53,7 25,4* 53,6 75l 52,7 49,2 54,8 61,4 57,2 47, ,8 60l 52,7 51,3 54, , ,3 45l 54,1 52,3 60,5 65,2 65,3 62, ,7 30l 54,3 51, ,6 63, ,4 62,6 15l 64,8 52,8 66,7 70,8 65,9 71,5 64,7 68,2 frontal 54, ,3 64,8 64,1 63, ,4 15r 58,6 48,2 58,8 61,7 59,1 52, ,4 30r 57,2 55,8 63,8 67,3 66,6 64, ,4 45r 56,5 52,3 58,8 64,5 63,3 58,5 58, r 53 51,8 58, ,8 56,3 52,8 57,8 75r 49, ,5 58,9 59,7 49,2 54,1 57,3 90r 50,1 47,7 52,3 56,8 53,1 52,1 23,5* 52,3 *something wrong here Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 24
25 Classification results training on high intensities features: raw shape coordinates & texture parameters Average accuracy over all poses Average accuracy over all poses & emotions All intensities used for training Intensities 2, 3, 4 used for training Intensities 3, 4 used for training Anger 64 65,3 61,7 Disgust 67,8 66,5 62 Fear 39 39,3 45,1 Happiness 73,1 69,3 62,8 Sadness 63,7 60,2 53,9 Surprise 76,2 74,1 68,4 Neutral 27 36,4 48,7 All intensities used for training Intensities 2, 3, 4 used for training Intensities 3, 4 used for training Intensity 1 55,1 52,1 44,7 Intensity 2 65,5 62,1 59,2 Intensity ,7 64,2 Intensity 4 70,8 71,6 70,6 Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 25
26 Classification results training on high intensities features: raw shape coordinates & texture parameters Average accuracy over all emotions All intensities Intensities 2, 3, 4 Intensities 3,4 used for training used for training used for training 90l 58,2 58,5 53,1 75l 61,4 59,5 57,6 60l 62 61,4 55,9 45l 65, ,6 30l 62, ,3 15l 70,8 72,5 66,5 frontal 64,8 65,5 62,2 15r 61,7 59,5 57,5 30r 67,3 66, r 64,5 63,3 58,1 60r 61 60,9 58,5 75r 58,9 50,2 53,5 90r 56,8 55,6 52,3 Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 26
27 Comparison: confusion matrices: my best result average related work My 'best' result (highest average accuracy (72,5%)): Intensities 2, 3, 4 used for training 15 left Accuracy(%) Groundtruth Recognized Anger Disgust Fear Happiness Sadness Surprise Neutral Anger 71,9 12, ,4 0 6,3 Disgust 11,1 80 4,4 2,2 1,1 1,1 0 Fear 10 14,3 51,4 8,6 4,3 5,7 5,7 Happiness 1,7 3,4 3,4 88,9 0,9 0 1,7 Sadness 14,3 3,9 9,1 1, ,4 Surprise 5,1 3,8 10,1 1,3 5,1 70,9 3,8 Neutral 6,3 6, ,8 0 68,8 Related work: average over combination of SIFT+LPP, LBP+LPP and HoG+LPP on 5 views Accuracy(%) Groundtruth Recognized Anger Disgust Fear Happiness Sadness Surprise Anger 73,5 5,6 3,9 0,8 15,4 0,9 Disgust 10,2 71 7,7 3,7 4,1 3,4 Fear 7 9,2 55,4 14,5 7,7 6,3 Happiness 1,7 3,1 11,6 81,7 1,1 0,7 Sadness 19,7 2,1 5,7 0,9 71,4 0,3 Surprise 1,3 2,3 5,5 1,5 1,7 87,7 Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 27
28 Conclusion Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 28
29 Conclusion Related work has better results, but: groundtruth points used for extracting features only 5 angles, bigger distances neutral expression not included General Problems: 'weak' expressions (fear, anger, sadness) have worse results than 'strong' (surprise, happiness) ones 'neutral': few data, very bad results will be dropped low intensities of expressions hard to recognize, even by humans Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 29
30 Conclusion When training on high intensity data: 'weak' expressions get better results, but others become worse results improve for highest intensity (training on int. 2, 3, 4), but others are worse Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 30
31 Future Work: Drop 'neutral' class Try different feature selection methods Try linear SVMs for classification Cross validation Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 31
32 Thank you for your attention! Any questions? Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 32
33 References Yuxiao Hu, Zhihong Zeng, Lijun Yin, XiaozhouWei, Jilin Tu, and T.S. Huang, Multi-view facial expression recognition, FG2008, 2008, ICPR 2008, 8th International Conference on Automatic Face and gesture Recognition, Sept Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 33
34 Done until now Done fitting for rest of data Performed LDA on data Classified fitted test-data using: shape-coordinates, shape parameters, texture parameters, shape coordinates & texture parameters, shape & texture parameters, SIFT, shape & texture parameters & SIFT Compared results Analysis of influence of emotion-intensities on classification Trained SVMs with high intensity-data only (234, 34) + classification Multi-view facial expression recognition using AAMs - Progress report 2 - Nikolas Hesse Seite 34
35 Data - Example Model rotated from -90 to 90, 15 steps, for each step image and shape are saved Multi-view facial expression recognition using AAMs - Progress report - Nikolas Hesse Seite 35
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