emotion recognition final

8/7/2019 emotion recognition final

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What is emotion ?

A basic emotion model from biometrics viewpoint.

Role of biosignals in detection of human emotions.

A typical biometric system architecture for emotionrecognition

Processing model of an ECG signal

Comparison of bio-signals for different emotions

Future scope and applications

Challenges and Limitations

Conclusion


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Emotion is the complex psychophysiological experienceof an individuals state of mind as interacting withbiochemical(internal) and environmental(external)influences .


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Emotion: a concept involving three components:

Subjective experience

Expressions ( audiovisual: face, gesture, posture, voiceintonation, breathing noise )

Biological arousal ( ANS: heart rate, respirationfrequency/intensity, perspiration, temperature, muscletension, brain wave ) High

arousal

Low

arousal

Negative Positive

TerrorAgitation

MournfulBliss

ExcitedAnticipation

Distressed

DisgustRelaxed


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Different emotional expressions produce differentchanges in autonomic activity:

Anger: increased heart rate and skin temperature

Fear: increased heart rate, decreased skin

temperature Happiness: decreased heart rate, no change in skin

temperature

Continuous data collection

Robust against human social artifact

Easily integrated with external channels (face and

speech)


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Photoplethysmography, bounces infra-red lightagainst a skin surface and measures the amount ofreflected light.

Palmar surface of fingertip Features: heart rate, vascular dilation (pinch),

vasoconstriction Cues:

Increasing BV- angry, stress Decreasing BV- sadness, relaxation


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Electrical voltages generated by brain cells(neurons) when they fire, frequencies between 1-40Hz

Frequency subsets: high beta (20-40Hz), beta(15-20Hz), Sensorimotor rhythm (13-15Hz),alpha (8-13Hz), theta (4-8Hz), delta (2-4Hz),EMG noise (> 40Hz)

Standard 10-20 EEG electrode placement Mind reading, biofeedback, brain computing

Raw

Alpha


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Muscle activity or frequency of muscle tension Amplitude changes are directly proportional to

muscle activity

On the face to distinguish between negative and

positive emotions

Recognition of facial expression, gesture andsign- language


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Measure of skins ability to conductelectricity

Linear correlated with arousal

Represents changes in sympathetic nervoussystem and reflects emotional responses andcognitive activity


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Relative measure of chest expansion On the chest or abdomen

Respiration rate (RF) and relative breathamplitude (RA)

Emotional cues: Increasing RF anger, joy

Decreasing RF relaxation, bliss


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Measure of skin temperature as itsextremities

Dorsal or palmar side of any finger or toe

Dependent on the state of sympatheticarousal

Increase of Temp: anger > happiness,sadness > fear surprise, disgust


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Measures contractile activity of the heart Provides two types of information :a) by measuring IBI , electrical activity is detected and shows if it

is normal, fast or slowb) stress carried by hear

Heart rate (HR), inter-beat intervals (IBI) and heart ratevariability (HRV), respiratory sinus arrhythmia

Emotional cues: Decreasing HR: relaxation, happy Increasing HRV: stress, frustration


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Analysis of various ECG waves and normal vectors ofdepolarization and repolarization yields importantemotional information.

Every emotion has a uniquePQRST pattern in terms of

amplitude, shape, consistency

and time interval between waves

By analyzing & comparing

the acquired ECG signal, emotion

of a person can be detected


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Biometric

Data Collection

TransmissionQualitySufficient?

Yes

Template Match

Signal Processing,Feature Extraction,

Representation

Database

Generate Template

Additional image preprocessing,adaptive extraction/representation

Require new acquisitionof biometric

Approx 512 bytes of dataper template

NoYes

DecisionConfidence?

No

Biometric Template: A fileholding a mathematicalrepresentation of the identifyingfeatures extracted from the rawbiometric data.

For practicalapplication/use


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Each participant were made to listen to four songsand their emotional characteristics were observedusing biometrics.

Basic four signals which were analyzed were :SC, EMG , RESP and ECG/EKG.

Song selection criteria song1: enjoyable, harmonic, dynamic, moving song2: noisy, loud, irritating, discord

song3: melancholic, reminding of sad memory song4: blissful, slow beat, pleasurable,

slumberous

An example of emotion recognition


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An example of emotion recognition


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Lie detector test can further be expanded to knowing exactlywhat frame of mind of the person is in.

Understanding abnormal behavior in human beings undercertain conditions.

Development of human machine interface gadgets.

In recognizing emotions from a speech or ECG signal inselection of a training corpus

In entertainment industry, several models for human interactivegames / rides have been designed and successfully implementedusing emotion recognition.

Research to understand the super human beings.


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Requires integration of various bio signals and bio sensingtechnologies. Hence, in practical applications, it becomes difficult tocustomize the entire system

Mood of human beings differs from one another and each has

different ways of expressing themselves under similar conditions.Hence, in many situations, same data can not be applied for say aperson from England and one from Thailand. Hence, database forpeople from different regions need to be rebuild and may not becommon for all.

Need to develop more accurate biosensors and unparticular, a multi-utility bio sensors.

Generally, emotion recognition requires huge data collection, storageand processing using some heavy software. So it becomes a challenge

if we want to implement emotion recognition on any portable device.


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Still there remains certain lack of knowledge regardinghuman body and its behavior in terms of biology.

However, we can say emotion recognition would help inunderstanding human behavior much better and open the

doors for several applications in near future.

Advancements in several fields like bio sensors, signalprocessing, image processing , human psychology and

human biology would collectively help in development of

emotion recognition systems.


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emotion recognition final

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