Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Emotion AI
Search
Javier Gonzalez-Sanchez
PRO
November 21, 2019
Education
0
24
Emotion AI
Universidad de Guadalajara, Centro Universitario Tonalá.
Jornadas de la Ciencia 2019.
Javier Gonzalez-Sanchez
PRO
November 21, 2019
Tweet
Share
More Decks by Javier Gonzalez-Sanchez
See All by Javier Gonzalez-Sanchez
CSC307 Lecture 15
javiergs
PRO
0
84
CSC307 Lecture 14
javiergs
PRO
0
220
CSC307 Lecture 13
javiergs
PRO
0
150
CSC307 Lecture 12
javiergs
PRO
0
220
CSC307 Lecture 11
javiergs
PRO
0
240
CSC307 Lecture 10
javiergs
PRO
0
310
CSC307 Lecture 09
javiergs
PRO
1
500
CSC307 Lecture 08
javiergs
PRO
0
330
CSC307 Lecture 07
javiergs
PRO
0
220
Other Decks in Education
See All in Education
Contentless Marketing
jonoalderson
0
880
Visualisation Techniques - Lecture 8 - Information Visualisation (4019538FNR)
signer
PRO
0
1.8k
2024年度春学期 統計学 第13回 不確かな測定の不確かさを測る ― 不偏分散とt分布 (2024. 7. 4)
akiraasano
PRO
0
110
week8@tcue2024
nonxxxizm
0
490
ブームだけで終わらせない、組織内でコーチングを活用する方法/How to Use Coaching in Your Organization Without It Being Just a Fad
yuko_yokouchi
1
190
week11_2024_.pdf
nonxxxizm
0
380
Gemini in Android Studio 使ってますか?
satoryo56
0
130
Matz に頼られたので張り切って2時間ほどドイツと日本の互いの Ruby 学習事情についてディスカッションした話
yasulab
1
320
Droid Knights 2024 Accessibility in Android
nanamare
0
290
英語学習から海外発表までの流れ
yasulab
18
4.1k
【株式会社コノセル】会社説明資料
koheimiyamoto
0
2.7k
Servizo de correo
irocho
0
470
Featured
See All Featured
Designing on Purpose - Digital PM Summit 2013
jponch
113
6.6k
Optimizing for Happiness
mojombo
373
69k
The Straight Up "How To Draw Better" Workshop
denniskardys
229
130k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
24
1.8k
Being A Developer After 40
akosma
72
580k
Adopting Sorbet at Scale
ufuk
71
8.8k
The Mythical Team-Month
searls
217
43k
A better future with KSS
kneath
231
17k
VelocityConf: Rendering Performance Case Studies
addyosmani
321
23k
The Pragmatic Product Professional
lauravandoore
29
6.1k
Raft: Consensus for Rubyists
vanstee
134
6.5k
Happy Clients
brianwarren
94
6.5k
Transcript
Emotion AI: Inteligencia Artificial y reconocimiento de emociones Javier Gonzalez-Sanchez,
PhD
[email protected]
www. javiergs.com
Dr. Javier Gonzalez-Sanchez Arizona State University | Universidad Panamericana
[email protected]
|
[email protected]
www.javiergs.com @mscjaviergs
Inteligencia 3 Conocimiento emocional Comprensión Aprendizaje Planificación Solución de problemas
Afecto 4
Emotion AI
Emotion AI El Afecto (Emociones y Sentimientos) controla nuestras decisiones
racionales
Decisiones racionales 7
Agenda 1. Inteligencia Artificial - ¿cómo funciona eso? 2. Reconocimiento
de Emociones - ¿funciona eso? 3. Y - ¿para qué me sirve eso?
Inteligencia Artificial ¿cómo funciona eso? 1
None
None
None
None
None
None
None
None
Temas
Temas
Neural Networks
Neural Network | Neuron input output
Neural Network | Neuron X1 X2 X3 x4
Neural Network | Neuron X1 X2 X3 x4 • Sumar
x1 + x2 + …
Neural Network | Neuron X1 X2 X3 x4 • Normalizar
Neural Network | Neuron X1=1 X2=0.5 X3=0.5 X4=-1 • Sumar
• Normalizar 0.73
Neural Network X1 X2 X3 X4 W1 W2 W3 W4
sig (W1*X1 + W2*X2 + W3*X3 + W4*X4) • Sumar • Multiplicar • Normalizar
Neural Network | Weights input output
Neural Network Y, ¿dónde está la inteligencia? En el aprendizaje
y razonamiento para calcular los valores de W Calcular los Valores W sig (W1*X1 + W2*X2 + W3*X3 + W4*X4)
Ejemplo
Ejemplo
Neural Network X1 X2 X3 X4 W1,1 W2,1 W3,1 W4,1
sig (W11*X1 + W21*X2 + W31*X3 + W41*X4) W1,2 W2,2 W3,2 W4,2 sig (W12*X1 + W22*X2 + W32*X3 + W42*X4) W [i][j] = W[weight][neuron]
Neural Network X1 X2 X3 X4 sig (W111*X1 + W121*X2
+ W131*X3 + W141*X4) W1,1,2 W1,2,2 W1,3,2 W1,4,2 W [k][i][j] = W[layer][weight] [neuron] sig ( ) sig (W112*X1 + W122*X2 + W132*X3 + W142*X4) sig ( ) W2,1,1 W2,2,1 W2,1,2 W2,2,2 layer_1 layer_2
Back propagation • Partial derivative of the cost function with
respect to any weight in the network. • We want to see how the function changes as we let just one of those variables change while holding all the others constant. • Black-box
Necesitamos Datos
Reconocimiento de Emociones ¿funciona eso? 2
Sensores, Percepción e Integración 36
Historia
1.1 Tu Cerebro te delata Ondas cerebrales 38
Tu Cerebro te delata 39
Tu Cerebro te delata 40 $ 799.00
Tu Cerebro te delata 41
Tu Cerebro te delata Timestamp AF3 F7 F3 FC5 T7
P7 O1 O2 P8 T8 FC6 F4 F8 AF4 AccX AccY 101116112544901 4542.05 4831.79 4247.18 4690.26 4282.56 4395.38 4591.79 4569.23 4360 4570.77 4297.44 4311.28 4282.56 4367.18 1660 2003 101116112544901 4536.92 4802.05 4243.08 4673.85 4272.31 4393.33 4592.82 4570.26 4354.87 4570.26 4292.31 4309.74 4277.95 4370.77 1658 2002 101116112545010 4533.33 4798.97 4234.87 4669.74 4301.03 4396.92 4592.31 4570.77 4351.28 4561.03 4281.54 4301.54 4271.28 4363.59 1659 2003 101116112545010 4549.23 4839.49 4241.03 4691.28 4333.85 4397.95 4596.41 4567.18 4355.9 4556.41 4286.15 4306.15 4277.95 4369.74 1659 2003 101116112545010 4580 4865.64 4251.79 4710.26 4340 4401.54 4603.59 4572.82 4360 4558.46 4298.97 4324.62 4296.41 4395.9 1657 2004 101116112545010 4597.44 4860 4252.82 4705.64 4350.26 4412.31 4603.59 4577.44 4357.44 4555.9 4295.38 4329.23 4296.41 4414.36 1656 2005 101116112545010 4584.62 4847.69 4246.67 4690.26 4360 4409.23 4597.44 4569.74 4351.79 4549.74 4278.97 4316.92 4272.82 4399.49 1656 2006 101116112545010 4566.15 4842.05 4238.46 4684.1 4322.05 4389.74 4592.82 4566.67 4351.79 4549.74 4274.36 4310.26 4262.05 4370.77 1655 2005 101116112545010 4563.59 4844.62 4231.79 4687.69 4267.69 4387.69 4594.36 4580 4361.03 4556.41 4278.97 4310.77 4274.36 4370.77 1653 2006 101116112545010 4567.18 4847.18 4233.33 4688.72 4285.13 4409.23 4602.05 4589.23 4368.21 4560 4280.51 4310.77 4281.54 4390.26 1655 2004 101116112545010 4562.05 4840.51 4227.18 4673.85 4300 4405.13 4611.28 4601.03 4376.41 4561.54 4280 4303.59 4279.49 4374.87 1652 2000
Tu Cerebro te delata 43 14 canales 128 lecturas por
segundo 1,792 valores por segundo 107,520 valores por minuto 6,451,200 valores por hora
Tu Cerebro te delata 44 https://askabiologist.asu.edu/brain-regions
Tu Cerebro te delata Timestamp Short Term Excitement Long Term
Excitement Engagement Meditation Frustration 101116091145065 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145190 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145315 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145440 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145565 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145690 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145815 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145940 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146065 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146190 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146315 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146440 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146565 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146690 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146815 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146941 0.451885 0.541695 0.848087 0.368071 0.533919
Tu Cerebro te delata 46 5 reportes por segundo 5
estados afectivos 25 valores por segundo 1,500 valores por minuto 90,000 valores por hora
Demo 47
1.2 Un mundo te observa Gestos Your Date Here Your
Footer Here 48
Un mundo te observa 49 (Ekman and Friesen 1978) –
Facial Action Coding System, 46 actions (plus head movements). 19 Lip Corner Depressor 26 Jaw Drop 27 Mouth Stretch
Un mundo te Observa
Un mundo te Observa Timestamp Agreement Concentrating Disagreement Interested Thinking
Unsure 101116112838516 0.001836032 0.999917 1.79E-04 0.16485406 0.57114255 0.04595062 101116112838578 0.001447654 0.9999516 1.29E-04 0.16310683 0.5958921 0.042706452 101116112838672 5.97E-04 0 1.5E-04 0.44996294 0.45527613 0.00789697 101116112838766 2.46E-04 0 1.75E-04 0.77445686 0.32144752 0.001418217 101116112838860 1.01E-04 0 2.04E-04 0.93511915 0.21167138 2.53E-04 101116112838953 4.18E-05 0 2.38E-04 0.983739 0.13208677 4.52E-05 101116112839016 1.72E-05 0 2.78E-04 0.9960774 0.07941038 8.07E-06 101116112839110 7.1E-06 0 3.24E-04 0.99906266 0.046613157 1.44E-06 101116112839156 2.92E-06 0 3.77E-04 0.99977654 0.026964737 2.57E-07 101116112839250 1.21E-06 0 4.4E-04 0.9999467 0.015464196 4.58E-08 101116112839391 4.97E-07 0 5.12E-04 0.9999873 0.008824189 8.18E-09 101116112839438 2.05E-07 0 5.97E-04 0.999997 0.005020725 1.46E-09 101116112839547 8.43E-08 0 6.96E-04 0.9999993 0.002851939 2.6E-10 101116112839578 3.47E-08 0 8.11E-04 0.9999999 0.001618473 4.64E-11 101116112839688 1.43E-08 0 9.45E-04 0.99999994 9.18E-04 8.29E-12 101116112839781 5.9E-09 0 0.001101404 1 5.21E-04 1.48E-12 101116112839828 2.43E-09 0 0.001283521 1 2.95E-04 2.64E-13
Un mundo te Observa 52 30 cuadros por segundo 10
inferencias por segundo 600 valores por minuto 36,000 valores por hora
Demo 53
1.3 Rastreo de Ojos ¿Qué estas viendo? Your Date Here
Your Footer Here 54
¿Qué estas viendo? 55 $ 199.00 $ 9,999.00
¿Qué estas viendo? Timestamp GPX GPY Pupil Left Validity L
Pupil Right Validity R Fixation Event AOI 101124162405582 636 199 2.759313 0 2.88406 0 48 Content 101124162405599 641 207 2.684893 0 2.855817 0 48 Content 101124162405615 659 211 2.624458 0 2.903861 0 48 Content 101124162405632 644 201 2.636186 0 2.916132 0 48 Content 101124162405649 644 213 2.690685 0 2.831013 0 48 Content 101124162405666 628 194 2.651784 0 2.869714 0 48 Content 101124162405682 614 177 2.829281 0 2.899828 0 48 Content 101124162405699 701 249 2.780344 0 2.907665 0 49 Content 101124162405716 906 341 2.853761 0 2.916398 0 49 Content 101124162405732 947 398 2.829427 0 2.889944 0 49 Content 101124162405749 941 400 2.826602 0 2.881179 0 49 Content 101124162405766 938 403 2.78699 0 2.87948 0 49 KeyPress Content 101124162405782 937 411 2.803387 0 2.821803 0 49 Content 101124162405799 934 397 2.819166 0 2.871547 0 49 Content 101124162405816 941 407 2.811687 0 2.817927 0 49 Content 101124162405832 946 405 2.857419 0 2.857427 0 49 Content 101124162405849 0 0 -1 4 -1 4 49 Content
¿Qué estas viendo? 57 30 o 60 cuadros por segundo
30 o 60 inferencias por segundo 1,800 o 3,600 valores por minuto 108,000 o 216, 000 valores por hora
Demo 58
Y ¿para qué me sirve eso? 3
Demo 60 BCI and Gaze Points engagement
Demo 61 BCI and Gaze Points frustration
Demo 62 BCI and Gaze Points engagement
Demo 63 BCI and Gaze Points frustration
Demo 64 BCI and Gaze Points
Demo 65 Emotion Adaptation | Games
Demo 66 Emotion Adaptation | Games
Demo 67
En Conclusión 68
Dr. Javier Gonzalez-Sanchez Universidad Panamericana | Arizona State University
[email protected]
|
[email protected]
www.javiergs.com @mscjaviergs !Gracias!