2004Unpublished venueRequires access

Authentic facial expression analysis

Nicu Sebe, Michael S. Lew, Ira L. Cohen, Yafei Sun, Theo Gevers, Thomas S. Huang

Open publisher page 101 citations

Abstract

It is argued that for the computer to be able to interact with humans, it needs to havve the communication skills o humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.

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What this paper is about

It is argued that for the computer to be able to interact with humans, it needs to havve the communication skills o humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.

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OpenAlex reports 101 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

It is argued that for the computer to be able to interact with humans, it needs to havve the communication skills o humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.

Key concepts: Facial expression, Facial Action Coding System, Expression (computer science), Computer science, Action (physics), Affective computing, Emotional expression, Emotion classification

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