Machines that Recognize Human Emotion

Machines that Recognize Human Emotion

Yuan Qi

MIT Media Laboratory

A man barges into your office when you’re busy. He doesn’t apologize, doesn’t introduce himself, and

doesn’t notice you are annoyed.

He offers you useless advice.You express more annoyance. He ignores it.

He continues to be unhelpful. The clarity of your emotional expression escalates. He ignores it.

(this goes on) Finally you have to tell him explicitly “go away”

He winks, and does a little dancebefore exiting.

Recognition of three “basic” states:

• Expressions, behaviors“Flared nostrils, tightened lips, a quick sharp

gesture, skin conductivity=high; probably she is angry ”

• Situation, reasoningThat was an important goal to her and Bob just

thwarted it, so she probably feels angry toward Bob

“ Emotion recognition”

Emotions give rise to changes that can be sensed

Face

Distance Voice

Sensing: Posture

Gestures, movement, behavior

Skin conductivity

Pupillary dilation

Up-close Respiration, heart rate, pulse

Sensing: Temperature

Blood pressure

Internal Hormones

Sensing: Neurotransmitters

…


Distance Voice

Sensing: Posture


Skin conductivity

Pupillary dilation



Blood pressure

Internal Hormones


…


Sensing: Posture


Skin conductivity

Pupillary dilation



Blood pressure

Internal Hormones


…



Skin conductivity

Pupillary dilation



Blood pressure

Internal Hormones


…


Skin conductivity Pupillary dilation



Blood pressure

Internal Hormones


...

Can a machine tell if a person is bored or interested?

Attentive? Fidgeting?

Application: Computer Learning Companion, Tutor, Mentor

Can we teach a chair to recognize behaviors indicative of interest and boredom? (Mota and Picard)

Sit upright Lean Forward Slump Back Side Lean

What can the sensor chair contribute toward inferring the user’s state: Bored vs. interested?

9-state Posture Recognition: 89-97% accurateHigh/Low interest, Taking a Break: 69-83% accurate(Results on kids not in training data, 2002)

Detecting, tracking, and recognizing facial expressions from video (Kapoor & Picard)

Computer recognition of natural head nods and shakes

Kapoor and Picard, PUI ‘01

Fully automatic computer recognition of six natural facial “action units”

(Kapoor and Picard)

Accuracy:“Expert” human: 75%Our first system: 67%

Can the computer sense mild frustration or distress?

(e.g., for usability testing in the field?)

Things to communicate frustration

(Reynolds & Picard)

Example: data from pressure mouse

Forthcoming paper w/Jack Dennerlein, Harvard School of Public Health, and Carson Reynolds/Rosalind Picard at MIT, International Ergonomics Association, linking frustration and physical risk factors

Can the computer sense other emotions? Stress? Pleasure?…

Sensing Processing Expression

Wearable skin conductivity communicator

Making the light glow:

• Significant thoughts

• Exciting events

• Exercise

• Motion artifacts

• Lying

• Pain

Audience’s “Glow” conveys excitement(Approximate Skin Conductivity Level)Audience’s “Glow” conveys excitement(Approximate Skin Conductivity Level)

Communicate emotion in new waysPicard and Scheirer, HCI 2001

Cybernetic wearable camera(Healey & Picard, ISWC 98)

StartleCam Filter

Video: StartleCam(Healey & Picard, ISWC 98)

Subject intentionally expressing 8 emotions:

1. Neutral 5. Platonic Love2. Anger 6. Romantic Love3. Hate 7. Joy4. Grief 8. Reverence

Each emotion collected daily, for > 4 weeks4 physiological signals:EMG on jaw, skin conductivity, BVP, respiration

Classification Accuracy:81% on 8 emotions (person dependent)Picard et al., IEEE Trans. Pattern Analysis Machine Intell.,Oct 2001.

1. Neutral 5. Platonic Love2. Anger 6. Romantic Love3. Hate 7. Joy4. Grief 8. Reverence

Autonomic Balance = LF/HF

Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data

(Y. Qi, T.P. Minka, and R.W. Picard 01) Problem

Estimating spectrum with data that is

• Nonstationary• Unevenly

Sampled• Noisy

Bayesian Approach

Dynamic modeling of the time series

Then the spectrum at time ti can be summarized by the posterior mean of p(si|x1:i ).

)2(

)1(

]2cos,...,2cos

,2sin,...,2sin,1[

],...,,,,...,,[

1

1

1

1010

iiii

iii

iMi

iMii

TiMiiiMiii

vscx

wss

tftf

tftfc

bbbaaas

w i : the process noise at time t i

vi : the observation noise at time ti.The filtering distribution p(si|x1:i ) can be sequentially estimated as

)|(

)|()|()|(

)|()|()|(

1:1

1:1:1

11:1111:1

ii

iiiiii

iiiiiii

xxp

xspsxpxsp

dsxspsspxsp

Comparison with Classical Spectrum Estimation Algorithms

Welch

Burg

Music

Multitaper

New

The signal is the sum of 19, 20, and 21 Hz real sinusoid waves with amplitudes 0.5, 1, and 1 respectively. The variance of the additive white noise is 0.1. The signal is evenly sampled 128 times at 50 Hz.

Lomb-Scargle periodogram with a

window size of 200 points

Lomb-Scargle periodogram with a window size of 200 points

Spectral analysis for an unevenly sampled signal

The signal frequency jumps from 20 Hz to 40 Hz at the sampling time -0.833 second, and then jumps from 40 Hz to 60 Hz at 0.833 second.

Spectrogram by the new method Spectrogram by the new method

coupled with sparsification

The signal frequency jumps from 20 Hz to 40 Hz at the sampling time -0.833 second, and then jumps from 40 Hz to 60 Hz at 0.833 second.

Spectral analysis for an unevenly sampled signal

Simultaneously examine physiology and behavior for recognizing level of stress: up to 96% accurate, across 12 drivers.(Healey and Picard, ICPR 2000)

Driver Stress Demo(work w/Jen Healey, Yuan Qi,incorporating new spectral estimationtechnique for assessing heart rate variability)

Stress is evident for this person when:driving through cityturning around at toll boothhearing siren

New algorithm: analysis of heart-rate variability via real-time spectrum estimation with missing and irregularly sampled data (Qi and Picard, 2001)

Goal: recognize stress in speech of driver,over cell phone headset.

Recognizing Affect in Speech: Stress

Data: Four drivers talking over cell phone (headset)

Problem: Associate stress with cognitive load of driving/verbal task: 2 speeds of driving (~60 kph, ~120 kph)2 speeds of questioning (every 9 sec, every 4 sec)

Models: Daubechies-4 filterbank: 21 bands, Teager Energy Operator features, Models: HMM, Auto-regressive HMM, Factorial HMM, Hidden-Markov Decision Tree, Support Vector Machine, Neural Network, Mixture of HMM’s

Results: 96% training/62% testing on 4 categories stress with Mixture HMM’s; highly speaker dependent, e.g. 89-100% training, 36-96% test

Fernandez & Picard, ISCA Workshop on Speech and Emotions, Belfast 2000

Extralinguistic Markers

BreathsPauses

F0

Intonation

Syllables

Tempo

Rhythmicality, …

Understanding the Structure of Spoken Language for Affect Modeling


FaceDistance VoiceSensing: Posture Gestures, movement, behavior

Skin conductivity Pupillary dilationUp-close Respiration, heart rate, pulseSensing: Temperature Blood pressure

Internal HormonesSensing: Neurotransmitters …

Conclusions & Challenges

• Steady progress w/sensors, pattern rec• Put the desires of the user first:

– more visible vs. less visible signals– non-tethered, wearable, portable, – psychological comfort– cognitive load/interruptions

• Still to combine w/additional context sensing & cognitive reasoning

Papers and projects/details:

http://www.media.mit.edu/affect

http://www.media.mit.edu/~yuanqi

•Machines that “have emotion”

•Emotion and consciousness

•Concerns

•Applications

•How to sense, recognize, build

•Modeling emotion

•Affective wearables

http://www.media.mit.edu/affect



Documents

Machines that Recognize Human Emotion