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The possibility of personality extraction using skeletal information in hip-hop dance by human or machine

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INTERACT2019で発表した際のプレゼン用スライドです。
This slide is for the presentation when presented at INTERACT2019.
「The possibility of personality extraction using skeletal information in hip-hop dance by human or machine」

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The possibility of personality extraction using skeletal information in hip-hop dance by human or machine

  1. 1. The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine Saeka Furuichi (Meiji University) Kazuki Abe, Satoshi Nakamura (Meiji University)
  2. 2. Dance is very popular all over the world Dance There has also been a huge demand for learning how to dance as well Breakdance Added as a new competition at 2018 Youth Olympics
  3. 3. How to improve dancing n To learn from skilled instructors Learners can practice choreographies that matches their level n To learn from dance videos on the Web …Popular with beginners and students because it can be easily challenged • Learner need to find appropriate choreographies themselves • Difficult to objectively identify whether the choreography suits them or not
  4. 4. System flow Target dancer who learning from dance videos Develop a system that can search for dance videos match their own personality User System 3. Analyzes personality 1. Dance in front of the camera 2. Upload dance videos to the system 4. System recommends videos with similar personalities For you!
  5. 5. Personality One of the key elements of dance Clear expression becomes more attractive Unclear expression tends to be superficial and boring In the previous breakdance example, personality is included as one of the judging criteria (Trivium Value System) It is important for dancers to develop their own personality
  6. 6. Related work Many dance studies using body motion data • Developed a method to make dance motions natural on VR by HMMs. [Mousas ‘18] • Classified dance motions related to emotions using LMA. [Aristidou ‘15] • Proposed a system for emotional behavior recognition. [Senecal ‘16] Few studies focused on dance personality The specific elements when the personality appears are not clear
  7. 7. Purpose The purpose of research To search for characteristic which can be personality from skeletal information in hip-hop dance 1. Can human distinguish personality? 2. Can machine distinguish personality? Easy to obtain dance skeletons by using Kinect, OpenPose and so on
  8. 8. Dataset Construction Skeletal information • Extracted from dance movement by Kinect • Composed of 15-points at 3D coordinates Participants • 22 university students (7 males, 15 females) • Let them dance the particular choreography five times
  9. 9. Dataset Construction Participants • Dance experience …Ranging from five months to six years (avg. 2.4 years) • Practice …Had 1-hour practice sessions twice a week for three weeks Particular choreography …Including many movements using the whole body such as raising the legs, squatting, turning, and hitting the chest Dataset: 22 participants × 5 times = 110 items (15 sec. × 110 items = 1650 sec.)
  10. 10. Dataset Construction Choreography used • Used from actually videos on the Web • Participants practiced in the club for school festival's performance • Music : Traila$ong’s “Gravity” Instructions • Asked participants to reproduce the choreography of the dance video • Asked participants to practice everyone together to make the same choreography
  11. 11. Let participants to select the dance that they thought their own one, and rank the skeletons from first to third place → To clarify whether it is possible to distinguish one’s own dance from video of only skeleton 1. Can human distinguish personality? select the dance 5 times
  12. 12. 1. Can human distinguish personality? • The selection of dance × 5 times • To rank the skeletons from first to third place • 1st=5 point, 2nd=3 point, 3rd=1 point The average point of each skeleton = “Score” 𝑆𝑐𝑜𝑟𝑒 = 𝑃𝑜𝑖𝑛𝑡 1 + 𝑃𝑜𝑖𝑛𝑡 2 + ⋯ + 𝑃𝑜𝑖𝑛𝑡 4 + 𝑃𝑜𝑖𝑛𝑡(5) 5
  13. 13. Let participants to select the dance that they thought their own one, and rank the skeletons from first to third place → To clarify whether it is possible to distinguish one’s own dance from video of only skeleton Interviewed for element to determine the distinction → To clarify where the personality appears in the skeleton 1. Can human distinguish personality?
  14. 14. 1. Can human distinguish personality? Divided participants into two groups the manner of expressing personality differs depending on the level of experience Little experience Rich experience 11 participants 11 participants Dance experience How much they can express the personality depends on the level of their dance experience
  15. 15. 1. Can human distinguish personality? Divided participants into two groups Little experience ・Never learned from a skilled teacher ・Delightful dance as a hobby Rich experience ・Learning from skilled instructors ・A lot of practice 11 participants 11 participants Dance experience
  16. 16. A's B's C's D's E's F's G's H's I's J's K's L's A 2.8 1.2 0.0 0.6 0.0 1.2 0.6 1.0 0.2 0.0 0.8 0.6 B 0.0 3.6 0.2 0.2 1.8 0.2 2.0 0.8 0.0 0.0 0.0 0.2 C 0.0 0.6 0.8 0.0 0.0 0.4 2.0 1.8 1.0 1.0 0.6 0.8 D 1.2 1.0 0.0 0.4 0.2 1.6 0.0 3.2 0.0 0.2 0.0 1.2 E 0.2 0.8 2.2 0.0 2.6 1.6 0.0 1.2 0.0 0.4 0.0 0.0 F 0.0 0.6 0.0 2.2 1.2 3.2 0.0 1.2 0.4 0.2 0.0 0.0 G 2.0 0.0 0.2 0.6 1.0 0.0 2.4 0.4 0.6 0.0 1.8 0.0 … … … … … … … … … … … … … K 0.0 0.2 0.0 0.0 0.6 2.8 0.0 3.4 0.0 1.0 0.0 1.0 1. ResultsEach participant The skeletal information that each participant thinks the answer • Participant A chose the answers 5 times • Add the score(average point) to each skeletal information
  17. 17. A's B's C's D's E's F's G's H's I's J's K's L's A 2.8 1.2 0.0 0.6 0.0 1.2 0.6 1.0 0.2 0.0 0.8 0.6 B 0.0 3.6 0.2 0.2 1.8 0.2 2.0 0.8 0.0 0.0 0.0 0.2 C 0.0 0.6 0.8 0.0 0.0 0.4 2.0 1.8 1.0 1.0 0.6 0.8 D 1.2 1.0 0.0 0.4 0.2 1.6 0.0 3.2 0.0 0.2 0.0 1.2 E 0.2 0.8 2.2 0.0 2.6 1.6 0.0 1.2 0.0 0.4 0.0 0.0 F 0.0 0.6 0.0 2.2 1.2 3.2 0.0 1.2 0.4 0.2 0.0 0.0 G 2.0 0.0 0.2 0.6 1.0 0.0 2.4 0.4 0.6 0.0 1.8 0.0 … … … … … … … … … … … … … K 0.0 0.2 0.0 0.0 0.6 2.8 0.0 3.4 0.0 1.0 0.0 1.0 1. ResultsEach participant The skeletal information that each participant thinks the answer • Highlight the highest score • Participant A chose her own skeleton as the final answer
  18. 18. A's B's C's D's E's F's G's H's I's J's K's L's A 2.8 1.2 0.0 0.6 0.0 1.2 0.6 1.0 0.2 0.0 0.8 0.6 B 0.0 3.6 0.2 0.2 1.8 0.2 2.0 0.8 0.0 0.0 0.0 0.2 C 0.0 0.6 0.8 0.0 0.0 0.4 2.0 1.8 1.0 1.0 0.6 0.8 D 1.2 1.0 0.0 0.4 0.2 1.6 0.0 3.2 0.0 0.2 0.0 1.2 E 0.2 0.8 2.2 0.0 2.6 1.6 0.0 1.2 0.0 0.4 0.0 0.0 F 0.0 0.6 0.0 2.2 1.2 3.2 0.0 1.2 0.4 0.2 0.0 0.0 G 2.0 0.0 0.2 0.6 1.0 0.0 2.4 0.4 0.6 0.0 1.8 0.0 H 0.0 0.2 0.0 0.6 0.4 0.2 0.0 2.6 0.0 0.0 4.2 0.8 I 0.2 1.2 0.8 0.6 0.0 0.6 1.0 2.6 0.0 1.6 0.2 0.2 J 0.0 0.6 0.2 0.0 2.0 1.0 1.0 0.8 0.6 0.6 2.2 0.0 K 0.0 0.2 0.0 0.0 0.6 2.8 0.0 3.4 0.0 1.0 0.0 1.0 a's b's c's d's e's f's g's h's i's j's k's l's a 0.4 3.0 0.6 0.0 0.0 0.0 1.2 1.8 0.2 0.0 1.2 0.6 b 0.0 0.6 0.0 0.0 1.6 0.6 0.4 0.0 0.8 0.0 4.2 0.8 c 0.0 0.6 2.4 0.6 1.0 0.0 2.0 1.0 0.4 0.2 0.8 0.0 d 0.2 0.6 1.2 0.6 1.0 0.2 0.0 1.0 1.2 1.2 1.0 0.8 e 0.0 0.0 0.2 0.0 1.4 0.2 0.0 3.2 0.6 0.4 2.0 1.0 f 0.0 0.0 0.0 0.6 0.0 0.8 1.6 0.4 0.6 0.2 2.8 2.0 g 0.2 0.0 0.0 0.0 0.0 3.2 0.6 0.0 0.2 0.2 2.4 2.2 h 0.0 0.2 0.6 0.0 0.0 0.0 0.2 2.6 0.0 0.2 1.2 4.0 i 0.0 0.0 0.0 0.2 0.0 0.0 1.6 0.0 4.6 0.2 2.4 0.0 j 0.2 0.6 0.0 1.2 0.8 0.0 0.0 2.2 0.0 0.0 2.2 1.8 k 0.0 1.0 0.6 0.0 0.0 2.2 2.2 0.2 2.0 0.0 0.8 0.0 1. Results Little experience 2 out of 11 participants gave the highest score Rich experience 5 out of 11 participants gave the highest score In both groups, the participants who did not choose their own dance tended to choose the specific one
  19. 19. nBody part used for discrimination nElement used for discrimination Hands Feet Chest Whole body Other Little 10 2 1 10 3 Rich 17(11) 4 2 8 3 Total 27 6 3 18 6 1. Results(interview) Shape Movement Speed Timing Habit Direction Center of gravity Little 8(6) 7 3 2 0 2 3 Rich 20(10) 1 5 3 4 2 1 Total 28 8 8 5 4 4 4 Little –avg. 2.3 cases ・Rich –avg. 3.2 cases
  20. 20. Little Rich Percentage of participants who chose their own dance 18% (2/11) 45% (5/11) Noticed body part Hand, Whole body Noticed elements Movement Shape 1. Results
  21. 21. Little Rich Percentage of participants who chose their own dance 18% (2/11) 45% (5/11) Noticed body part Hand, Whole body Noticed elements Movement Shape 1. Results Rich experience group were able to identify their own dance more accurately
  22. 22. Little Rich Percentage of participants who chose their own dance 18% (2/11) 45% (5/11) Noticed body part Hand, Whole body Noticed elements Movement Shape 1. Results Rich experience group were able to identify their own dance more accurately Generate machine learning's features using these two elements of concentrated answers e.g., movement of arms, knees e.g., angle of hands
  23. 23. 2. Can machine distinguish personality? Classify the dance by generating feature quantities based on discrimination factors‘ Used the 3D skeletal information obtained by Kinect nMovement -> Movement amount features Amount of spatial movement of each skeletal point every second nShape -> Joint angle features Average of joint angles every second (30 frames)
  24. 24. 2. Can machine distinguish personality? • Divided participants into the same two groups as the subjective evaluation experiment • Used Random Forest as the classification algorithm • Performed 12-value classification learning • To determine how well dancers can be identified from skeletal information Little experience Rich experience
  25. 25. 2. Generating feature quantities Movement features …Generated 13D vectors by calculating the amount of spatial movement 182 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠 𝑜𝑓 14 𝑠𝑒𝑐𝑜𝑛𝑑𝑠 × 13 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠 shoulderelbow knee hand hip foot torso
  26. 26. 2. Generating feature quantities Angle features …Generated 6D vectors by calculating the joint angles shoulder elbow knee
  27. 27. 2. Generating feature quantities Angle features …Generated 6D vectors by calculating the joint angles 84 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠 = 14 sec × 6 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠 1sec (6D vector) 14set (0-13) name cnt elbow0L elbow0R shoulder0L shoulder0R knee0L knee0R … knee13R 0 a 1 0.405474 0.746739 2.043996 1.654233 0.473768 0.724654 … 0.905857 1 a 2 0.156565 0.919898 1.801457 1.539244 0.718211 0.813780 … 0.987754 2 a 3 0.675029 0.559499 1.715216 2.000154 0.446872 0.599390 … 1.044120 3 a 4 0.542914 0.537184 1.742386 1.649719 0.464316 0.701625 … 0.265405 4 a 5 0.355517 1.151986 1.825681 1.625627 0.632807 0.653617 … 0.758982 5 b 1 0.310615 0.636567 2.267780 1.821457 0.312968 0.665087 … 1.047929 … … … … … … … … … … …
  28. 28. 2. Generating feature quantities As two countermeasures for noise • Linear interpolation from the frame before and after the defect frame • Smoothing by the method of exponential moving average Before After 2D
  29. 29. 2. Can machine distinguish personality? 10 (= 𝐶@ A) combinations of training and test data can be made from data per person • Learned 10 patterns each time • Calculated classification probability and average accuracy rate from the results Dance 1 Dance 2 Dance 3 Dance 4 Dance 5 Training data Test data
  30. 30. 2. Results The classification probability A's B's C's D's E's F's G's … L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 ... 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 ... 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 ... 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 ... 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 ... 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 ... 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 ... 0.15 … ... ... … ... ... ... ... … ... L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 ... 0.25
  31. 31. 2. Results The classification probability A's B's C's D's E's F's G's … L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 ... 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 ... 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 ... 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 ... 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 ... 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 ... 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 ... 0.15 … ... ... … ... ... ... ... … ... L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 ... 0.25 Each participant to be classified
  32. 32. 2. Results The classification probability A's B's C's D's E's F's G's … L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 ... 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 ... 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 ... 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 ... 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 ... 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 ... 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 ... 0.15 … ... ... … ... ... ... ... … ... L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 ... 0.25 Each dance of skeletal information
  33. 33. 2. Results The classification probability A's B's C's D's E's F's G's … L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 ... 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 ... 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 ... 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 ... 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 ... 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 ... 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 ... 0.15 … ... ... … ... ... ... ... … ... L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 ... 0.25 The total of the table row direction is normalized as 1.0
  34. 34. 2. Results The classification probability A's B's C's D's E's F's G's … L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 ... 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 ... 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 ... 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 ... 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 ... 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 ... 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 ... 0.15 … ... ... … ... ... ... ... … ... L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 ... 0.25 Highlight the highest score
  35. 35. The average accuracy rate: Little: 95.4% /Rich: 89.5% 2. Results A's B's C's D's E's F's G's H's I's J's K's L's A 0.22 0.08 0.07 0.07 0.10 0.06 0.06 0.08 0.08 0.08 0.03 0.06 B 0.08 0.31 0.05 0.07 0.07 0.08 0.05 0.06 0.05 0.08 0.04 0.06 C 0.07 0.06 0.31 0.06 0.08 0.07 0.07 0.04 0.08 0.04 0.06 0.04 D 0.06 0.07 0.05 0.19 0.05 0.09 0.09 0.11 0.07 0.09 0.06 0.06 E 0.09 0.08 0.08 0.07 0.35 0.05 0.05 0.05 0.06 0.06 0.03 0.04 F 0.06 0.08 0.05 0.10 0.05 0.26 0.07 0.08 0.05 0.08 0.06 0.05 G 0.07 0.08 0.07 0.11 0.06 0.08 0.15 0.09 0.06 0.08 0.08 0.07 H 0.08 0.06 0.05 0.11 0.05 0.08 0.08 0.26 0.05 0.07 0.06 0.07 I 0.08 0.07 0.08 0.08 0.07 0.07 0.06 0.06 0.21 0.06 0.08 0.08 J 0.07 0.07 0.04 0.10 0.06 0.08 0.08 0.09 0.06 0.22 0.06 0.08 K 0.03 0.05 0.07 0.08 0.03 0.08 0.09 0.06 0.09 0.07 0.30 0.05 L 0.08 0.07 0.05 0.08 0.05 0.08 0.07 0.09 0.08 0.09 0.06 0.20 a's b's c's d's e's f's g's h's i's j's k's l's a 0.34 0.01 0.08 0.13 0.09 0.05 0.08 0.01 0.06 0.06 0.05 0.05 b 0.01 0.42 0.03 0.03 0.05 0.05 0.06 0.12 0.04 0.06 0.06 0.06 c 0.06 0.03 0.32 0.11 0.07 0.08 0.12 0.03 0.07 0.03 0.03 0.05 d 0.12 0.04 0.11 0.22 0.09 0.07 0.10 0.04 0.05 0.06 0.05 0.06 e 0.08 0.06 0.06 0.09 0.23 0.05 0.10 0.05 0.06 0.08 0.07 0.08 f 0.03 0.05 0.06 0.05 0.05 0.30 0.09 0.08 0.08 0.09 0.07 0.05 g 0.06 0.05 0.10 0.08 0.09 0.09 0.25 0.04 0.07 0.07 0.04 0.06 h 0.01 0.12 0.03 0.03 0.05 0.09 0.04 0.35 0.07 0.06 0.06 0.09 i 0.05 0.05 0.07 0.06 0.07 0.10 0.09 0.07 0.24 0.11 0.05 0.06 j 0.05 0.07 0.03 0.06 0.07 0.09 0.08 0.06 0.10 0.26 0.08 0.05 k 0.04 0.05 0.02 0.04 0.06 0.06 0.03 0.05 0.04 0.08 0.43 0.10 l 0.04 0.05 0.05 0.05 0.07 0.06 0.07 0.08 0.05 0.05 0.10 0.34 Little experience Rich experience By movement features Each participant’s own dance had the highest classification probability Both groups
  36. 36. The average accuracy rate: Little: 99.1% /Rich: 92.0% 2. Results A's B's C's D's E's F's G's H's I's J's K's L's A 0.26 0.04 0.04 0.14 0.09 0.05 0.04 0.08 0.09 0.08 0.05 0.03 B 0.03 0.37 0.07 0.04 0.04 0.07 0.06 0.05 0.05 0.06 0.09 0.06 C 0.04 0.07 0.32 0.04 0.04 0.05 0.09 0.03 0.06 0.10 0.08 0.08 D 0.14 0.04 0.05 0.24 0.10 0.05 0.05 0.08 0.10 0.08 0.05 0.03 E 0.08 0.04 0.04 0.09 0.37 0.08 0.03 0.07 0.08 0.07 0.04 0.02 F 0.05 0.07 0.05 0.05 0.08 0.34 0.03 0.05 0.08 0.09 0.09 0.03 G 0.04 0.07 0.09 0.04 0.03 0.05 0.24 0.06 0.06 0.09 0.09 0.15 H 0.09 0.07 0.03 0.09 0.08 0.06 0.05 0.26 0.10 0.08 0.06 0.03 I 0.08 0.05 0.06 0.09 0.08 0.07 0.05 0.11 0.18 0.10 0.08 0.05 J 0.08 0.05 0.08 0.07 0.07 0.08 0.07 0.07 0.09 0.20 0.08 0.06 K 0.05 0.09 0.08 0.04 0.04 0.08 0.07 0.06 0.08 0.09 0.25 0.08 L 0.03 0.08 0.10 0.03 0.03 0.04 0.16 0.05 0.06 0.08 0.10 0.25 a's b's c's d's e's f's g's h's i's j's k's l's a 0.25 0.07 0.10 0.05 0.05 0.09 0.08 0.07 0.05 0.09 0.05 0.06 b 0.05 0.40 0.06 0.04 0.07 0.07 0.06 0.07 0.02 0.06 0.04 0.06 c 0.08 0.07 0.27 0.03 0.04 0.08 0.07 0.07 0.06 0.09 0.06 0.07 d 0.06 0.06 0.04 0.31 0.08 0.06 0.08 0.08 0.07 0.06 0.06 0.03 e 0.05 0.08 0.04 0.07 0.26 0.08 0.09 0.06 0.05 0.08 0.09 0.05 f 0.07 0.06 0.06 0.04 0.06 0.24 0.09 0.10 0.04 0.09 0.08 0.06 g 0.06 0.06 0.05 0.06 0.08 0.09 0.30 0.07 0.05 0.07 0.07 0.07 h 0.05 0.07 0.06 0.05 0.05 0.10 0.07 0.26 0.06 0.08 0.08 0.09 i 0.05 0.03 0.07 0.07 0.05 0.07 0.07 0.07 0.28 0.11 0.09 0.04 j 0.06 0.05 0.08 0.04 0.06 0.09 0.06 0.09 0.07 0.26 0.08 0.06 k 0.03 0.03 0.05 0.04 0.07 0.08 0.06 0.07 0.05 0.07 0.38 0.06 l 0.04 0.06 0.06 0.02 0.05 0.05 0.07 0.10 0.03 0.07 0.08 0.36 Each participant’s own dance had the highest classification probability Both groups Little experience Rich experience By angle features
  37. 37. 2. Results The average classification accuracy Machine learning can distinguish individuals Little Rich Movement feature 95.4% 89.5% Angle feature 99.1% 92.0%
  38. 38. 2. The reason rich experience group was worse • Compared to the little experience group, the rich experience group included more data with missing values • The dance that was wrongly estimated was concentrated on the data had missing values
  39. 39. 2. Correctly estimated number In the rich group Except for BCEF where all dances were correct for both features Movement features Angle features 1 2 3 4 5 A 1 4 4 4 4 D 4 4 1 4 4 G 4 4 4 4 3 H 4 4 4 4 4 I 4 0 4 1 4 J 3 4 4 4 4 K 4 4 3 4 4 L 3 4 4 2 4 1 2 3 4 5 A 4 4 3 4 2 D 4 3 3 4 2 G 4 3 2 4 0 H 4 4 4 2 4 I 4 0 4 4 4 J 4 4 0 4 3 K 4 4 4 4 4 L 4 4 4 4 4
  40. 40. When separated by a dance count instead of every second with features delimited every 4 counts (64 frames) with features delimited every 2 counts (32 frames) Little Rich Movement 87% 72% Angle 94% 78% Little Rich Movement 94% 87% Angle 99% 85% 2. Results
  41. 41. 2. Results(effective features) The random forest is ensemble learning that learns by a set of decision trees The importance of each feature vector can be evaluated by comparing each decision tree Training Data Result 1 Result 2 Result 3 Final Result
  42. 42. 2. Results(effective features) n Movement features The upper body such as the chest and right shoulder etc. n Angle features The left and right knees
  43. 43. Discuss the personality nCan human distinguish personality? Human can find features subjectively from dances with only skeletal information →Many did not correctly identify their own dance nCan machine distinguish personality? Machine can discriminate dances with high accuracy regardless of the features used It should become easier to find the personality of one’s own dance by using these features
  44. 44. Where is the personality? Consider the position and elements of the body where personality tends to appear nCan human distinguish personality? nCan machine distinguish personality? Movement Angle Hands Whole body Shape Movement Little 10 10 8 7 Rich 17 8 20 1 Considered to correspond to the whole body emphasized in the subjective evaluation, because the ratio occupied in the skeleton used for the subjective evaluation is large
  45. 45. To make this study Several challenges • Participants danced specific choreography → Necessary to conduct experiments with variety of choreography, and to clarify whether it is possible to identify the dancer
  46. 46. To make this study Several challenges • Participants danced specific choreography → Necessary to conduct experiments with variety of choreography, and to clarify whether it is possible to identify the dancer Future work • Clarify what constitutes personality in dance • Examine the application method of the extracted personality • Consider the method to search for dance videos that match the personality of the user
  47. 47. Conclusion Examined hip-hop dance to clarify the possibility to extract personality nCan human distinguish personality? • Human can distinguish the specific dance in the only skeletal information • The little group focused the movement • The rich group focused the shape nCan machine distinguish personality? • Machine can discriminate individuals with high accuracy using either of two features, movement amount and joint angle

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