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Deep Understanding of Persons in an Image Analysis Using Machine Learning Methods

Student: Ismail Kayali

Supervisor: Sergei Kuznetsov

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2018

As the proverb goes: "an image is deserving of a thousand words" Given an image of a person, there are centuries of attributes that can be predicted and analyzed: age, gender, body shape, clothes, skin color, hairstyle, attractiveness, expressions, gestures, impressions, personality traits, etc. This research aims to explain the dynamic transient expressions of the facial data corpus from real-world conditions. To catch the face shape and features resolutions, generating a dynamic version of the actual face image. This model is then fed into the conventional deep neural networks to achieve a useful classification of different facial expressions.

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