Enhancing Gloss-Based Corpora with Facial Features Using Active Appearance Models
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1 Enhancing Gloss-Based Corpora with Facial Features Using Active Appearance Models Christoph Schmidt, Oscar Koller, Hermann Ney 1 Thomas Hoyoux, Justus Piater Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University, Germany {surname}@i6.informatik.rwth-aachen.de 2 Intelligent and Interactive Systems University of Innsbruck, Austria {firstname}.{surname}@uibk.ac.at Schmidt, Koller Enhancing Gloss-Based Corpora 1 /
2 SignSpeak System Architecture Sign Language Video Feature extraction (Image Analysis) Sign Language Recognition Sign Language Translation Spoken Language Text Radboud University Nijmegen features glosses: MONDAY CHANGE SUN CLOUD spoken text: On Monday, the weather is changeable, partly sunny, partly cloudy. Scientific Understand of Sign Language Market Research & Prototype Development Dissemination and Feedback from the Deaf Community EUROPEANUNIONOFTHEDEAF Goal: translate a sign language video into a spoken language text Project Duration: April 2009-March 2012 Schmidt, Koller Enhancing Gloss-Based Corpora 2 /
3 Active Appearance Models track salient points on the face extract high-level facial features: mouth vertical openness mouth horizontal openness lower lip to chin distance upper lip to nose distance left eyebrow state right eyebrow state gap between eyebrows necessary: labeled data Schmidt, Koller Enhancing Gloss-Based Corpora 3 /
![RWTH-Phoenix-Weather Corpus DGS German signers 7 editions 190 duration[h] 3.](/docs-images/95/125342636/images/4-0.jpg "25 frames 293,077 sentences 2,711 glosses / words 17,744 33,190 vocabulary size 463 1,494 singletons 537 536 video-based, large vocabulary corpus weather forecasts from public TV news, interpreted")
4 RWTH-Phoenix-Weather Corpus DGS German signers 7 editions 190 duration[h] 3.25 frames 293,077 sentences 2,711 glosses / words 17,744 33,190 vocabulary size 463 1,494 singletons video-based, large vocabulary corpus weather forecasts from public TV news, interpreted into DGS annotation: glosses, time boundaries on gloss level focus on hand-based features Teaser: new version with 645 editions coming soon at LREC 2014! Schmidt, Koller Enhancing Gloss-Based Corpora 4 /
5 Mouthing variants ALPEN ( Alps ) Some signs only differ in mouthing / mouth gestures BERG ( mountain ) Annotation of RWTH-Phoenix-Weather focused on hand-based features Manual refinement of annotation time consuming Idea: automatic refinement using feature extraction and clustering Avatar animation: use refined annotation to animate mouthings / facial expressions Schmidt, Koller Enhancing Gloss-Based Corpora 5 /
6 Clustering Approach Cluster variants using AAM features Use the context of the spoken language to drive the clustering For avatar animation: select representative video Define distance between two videos: Train Hidden Markov Model on one video Calculate Viterbi path of second video Schmidt, Koller Enhancing Gloss-Based Corpora 6 /
7 Clustering Approach Align corpus EVENING RIVER THREE MINUS SIX MOUNTAIN Tonight three degrees at the Oder, minus six degrees at the Alps. Extract variants EVENING_tonight RIVER_Oder EVENING_evening RIVER_Rhein MOUNTAIN_mountains Cluster variants SL Spoken MOUNTAIN_Eifel MOUNTAIN_Erzgebirge MOUNTAIN_Erzgebirge MOUNTAIN_Erzgebirge MOUNTAIN_Eifel MOUNTAIN_Eifel MOUNTAIN_Eifel MOUNTAIN_Berge MOUNTAIN_Berge MOUNTAIN_Berge MOUNTAIN_Berge Schmidt, Koller Enhancing Gloss-Based Corpora 7 /
8 Clustering Approach Align corpus EVENING RIVER THREE MINUS SIX MOUNTAIN Tonight three degrees at the Oder, minus six degrees at the Alps. Extract variants EVENING_tonight RIVER_Oder EVENING_evening RIVER_Rhein MOUNTAIN_mountains Cluster variants SL Spoken MOUNTAIN_Eifel MOUNTAIN_Erzgebirge MOUNTAIN_Erzgebirge MOUNTAIN_Erzgebirge MOUNTAIN_Eifel MOUNTAIN_Eifel MOUNTAIN_Eifel MOUNTAIN_Berge MOUNTAIN_Berge MOUNTAIN_Berge MOUNTAIN_Berge Schmidt, Koller Enhancing Gloss-Based Corpora 7 /
9 Clustering Approach Align corpus EVENING RIVER THREE MINUS SIX MOUNTAIN Tonight three degrees at the Oder, minus six degrees at the Alps. Extract variants EVENING_tonight RIVER_Oder EVENING_evening RIVER_Rhein MOUNTAIN_mountains STRONG_forceful Cluster variants Spoken SL m: *puffed cheeks* m: forceful m: forceful m: forceful m: *puffed cheeks* m: *puffed cheeks* Schmidt, Koller Enhancing Gloss-Based Corpora 7 /
10 Clustering Approach Align corpus EVENING RIVER THREE MINUS SIX MOUNTAIN Tonight three degrees at the Oder, minus six degrees at the Alps. Extract variants EVENING_tonight RIVER_Oder EVENING_evening RIVER_Rhein MOUNTAIN_mountains STRONG_forceful Cluster variants Spoken SL m: *puffed cheeks* m: forceful m: forceful m: forceful m: *puffed cheeks* m: *puffed cheeks* Schmidt, Koller Enhancing Gloss-Based Corpora 7 /
11 Clustering Approach Align corpus EVENING RIVER THREE MINUS SIX MOUNTAIN Tonight three degrees at the Oder, minus six degrees at the Alps. Extract variants EVENING_tonight EVENING_evening RIVER_Oder RIVER_Rhein MOUNTAIN_mountains STRONG_forceful m: forceful m: forceful m: forceful m: *puffed cheeks* Cluster variants Spoken SL m: *puffed cheeks* m: *puffed cheeks* Clustering algorithm: adaptive medoid-shift Select medoid of biggest cluster as representative video Schmidt, Koller Enhancing Gloss-Based Corpora 7 /
12 Experiments Annotate mouthings to evaluate clustering quality Select the most frequent glosses with more than one mouthing Select the most frequent contexts glosses 23 (gloss,translation) pairs 64 running glosses 640 GLOSS context MOUNTAIN Alps " mountain RIVER Rhine " Oder RAIN rain " shower EVENING evening " night Schmidt, Koller Enhancing Gloss-Based Corpora 8 /
13 Clustering results Precision [%] avg:65.3% Recall [%] avg:82.6% F Measure [%] avg:67.8% (gloss,translation) (gloss,translation) (gloss,translation) Precision: only same mouthings are in same cluster Recall: only different mouthings are in different clusters F-Measure: geometric mean of precision and recall Schmidt, Koller Enhancing Gloss-Based Corpora 9 /
14 Clustering results: biggest cluster Accuracy [%] avg:78.4% (gloss,translation) Accuracy: medoid has same mouthing as other cluster members The overall algorithm achieves accuracy of 78.4% Schmidt, Koller Enhancing Gloss-Based Corpora 10 /
15 Clustering results: Examples left video: (MOUNTAIN,Allgaeu) right video: medoid of biggest cluster Algorithm can recognize same mouthing even among different signers Schmidt, Koller Enhancing Gloss-Based Corpora 11 /
16 Conclusion / Outlook Conclusions: Clustering algorithm to detect variants in facial features Select representative video for avatar animation Achieves high accuracy Outlook: improve low-level features: histogram of mouth area improve high-level features: HMM visemes apply method beyond mouthings: facial expressions, head shake, etc. Schmidt, Koller Enhancing Gloss-Based Corpora 12 /
17 Thank you for your attention Christoph Schmidt Schmidt, Koller Enhancing Gloss-Based Corpora 13 /
18 Appendix: Annotated glosses GLOSS BIT NORTH BUT NOW CALAMITY RAIN CAN RIVER COLD SKY COURSE SNOW DRY SOUTH ESPECIALLY STRONG EVENING SUN HIGH TEMPERATURE MORE WIND MOUNTAIN GLOSS ABEND MEHR ABER NORD BERG REGEN BESONDERS SCHNEE BISSCHEN SONNE FLUSS STARK GEWITTER SUED HIMMEL TEMPERATUR HOCH TROCKEN JETZT VERLAUF KALT WIND KOENNEN Schmidt, Koller Enhancing Gloss-Based Corpora 14 /
19 Appendix: Cluster Evaluation Precision = T P T P + F P, Recall = T P T P + F N, F-Measure = 2P R P + R True Positive: same mouthings is in same cluster True Negative: different mouthings is in different cluster False Positive: different mouthings are in same cluster False Negative: same mouthings are in different cluster Schmidt, Koller Enhancing Gloss-Based Corpora 15 /
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