Work

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Year: 2014

Type: conference-paper

Abstract: We investigate the use of two visual descriptors: Local Binary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information generated by the two descriptors using Fisher Vector encoding which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to introduce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method.

Source: International Workshop on Audio/Visual Emotion Challenge (AVEC)

DOI: 10.1145/2661806.2661817

Authors: Varun Jain, James L. Crowley, Anind K. Dey, Augustin Lux

Institutions: Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'Université Grenoble Alpes, Carnegie Mellon University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2060779380

FWCI: 3.486

Cites: 19

Cited by: 80

Related to: 10