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D. Helm, M. Kampel:
"Single-Modal Video Analysis of Personality Traits using Low-Level Visual Features";
in: "2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)", 1; K. Djemal et al. (ed.); issued by: IEEE; IEEE, U.S., 2020, ISBN: 978-1-7281-8750-1, 1 - 6.

The ability to analyze the first impression of a person
automatically enables novel applications in human-computer
interaction and other areas. A person´s first impression can
decide about a positive or negative outcome in different dailylife
situations. The human brain is able to get a picture of the
counterpart´s personality at short notice. The main aim of this
paper is to show how a system based on a standard Convolutional
Neural Network (CNN) as well as a 3D-CNN architecture, can be
built to solve a multi-label regression task using only visual lowlevel
features. This paper investigates how various pre-processing
methods, such as face-extraction and data-augmentation, influence
the predicted personality confidences. Furthermore, it
explores different training strategies and optimization techniques
e.g. regularization in order to improve the model performance.
The results of this paper demonstrate an image-based as well as
a time-sequence-based system to predict the Big-Five personality
dimensions of humans in short video sequences by solving a
multi-label regression task. The approaches reach an overall
accuracy of 0.891 by using visual features only.

Pattern recognition, face recognition, facial-expression recognition