Arousal-valence recognition using CNN with STFT feature-combined image

A novel ocular-features-combining method, called short-time Fourier transform (STFT) feature-combined image, and a simple convolutional neural networks (CNNs) model are proposed for arousal-valence recognition. The STFT feature-combined image aims to represent information on two ocular features (pup...

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Bibliographic Details
Published in:Electronics letters 2018-02, Vol.54 (3), p.134-136
Main Authors: Lee, H.-J, Lee, S.-G
Format: Article
Language:English
Subjects:
arousal‐valence recognition
CNN
emotion recognition
Fourier transforms
Image and vision processing and display technology
neural nets
ocular‐feature‐based emotion recognition
short‐time Fourier transform
simple convolutional neural networks
STFT feature‐combined image
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Summary:A novel ocular-features-combining method, called short-time Fourier transform (STFT) feature-combined image, and a simple convolutional neural networks (CNNs) model are proposed for arousal-valence recognition. The STFT feature-combined image aims to represent information on two ocular features (pupil size and eye movements) as a single image. The CNN model consists of two convolutional layers and uses STFT feature-combined image as an input. The experimental results demonstrate the effectiveness of the proposed method, and show that CNN model is not only effective for emotion-recognition methods based on other modalities, but also effective for ocular-feature-based emotion recognition.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2017.3538