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Micro-Expression Extraction For Lie Detection Using Eulerian Video (Motion and Color) Magnication
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
2014 (Engelska)Självständigt arbete på avancerad nivå (masterexamen)Studentuppsats (Examensarbete)Alternativ titel
Micro-Expression Extraction For Lie Detection Using Eulerian Video (Motion and Color) Magnication (Svenska)
Abstract [en]

Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and artificial intelligence, with newer and faster algorithms, have compelled mankind to search and adapt to contemporary methods in lie-detection. Observing the facial expressions of emotions in a person without any physical contact and implementing these techniques using artificial intelligence is one such method. The concept of magnifying a micro expression and trying to decipher them is rather premature at this stage but would evolve in future. Magnification using EVM technique has been proposed recently and it is rather new to extract these micro expressions from magnified EVM based on HOG features. Till date, HOG features have been used in conjunction with SVM, and generally for person/pedestrian detection. A newer, simpler and contemporary method of applying EVM with HOG features and Back-propagation Neural Network jointly has been introduced and proposed to extract and decipher the micro-expressions on the face. Micro-expressions go unnoticed due to its involuntary nature, but EVM is used to magnify them and makes them noticeable. Emotions behind the micro-expressions are extracted and recognized using the HOG features \& Back-Propagation Neural Network. One of the important aspects that has to be dealt with human beings is a biased mind. Since, an investigator is also a human and, he too, has to deal with his own assumptions and emotions, a Neural Network is used to give the investigator an unbiased start in identifying the true emotions behind every micro-expression. On the whole, this proposed system is not a lie-detector, but helps in detecting the emotions of the subject under test. By further investigation, a lie can be detected.

Abstract [sv]

This thesis uses a magnification technique to magnify the subtle, faint and spontaneous facial muscle movements or more precisely, micro-expressions. This magnification would help a system in classifying them and estimating the emotion behind them. This technique additionally magnifies the color changes, which could be used to extract the pulse without a physical contact with the subject. The results are presented in a GUI.

Ort, förlag, år, upplaga, sidor
2014. , s. 72
Nyckelord [en]
Micro Expressions, Emotions, Eulerian Video Magnification, Histogram of Oriented Gradients, Voila-Jones Algorithm, Artificial Neural Networks, Support Vector Machines, Pattern Recognition and Classification
Nyckelord [sv]
Master of Science Programme in Electrical Engineering with emphasis on Signal Processing /Masterprogram i Elektroteknik med inriktning mot signalbehandling
Nationell ämneskategori
Datavetenskap (datalogi) Psykologi Signalbehandling
Identifikatorer
URN: urn:nbn:se:bth-3467Lokalt ID: oai:bth.se:arkivex5901B886159F9F55C1257D51004993E8OAI: oai:DiVA.org:bth-3467DiVA, id: diva2:830774
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Gautam: +46(0)739528573, +91-9701534064 Tushal: +46(0)723219833, +91-9000242241 Venu: +46(0)734780266, +91-9298653191 Sai: +91-9989410111Tillgänglig från: 2015-04-22 Skapad: 2014-09-12 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Institutionen för tillämpad signalbehandling
Datavetenskap (datalogi)PsykologiSignalbehandling

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