emotion recognition final
TRANSCRIPT
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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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