SmileNet: Registration-Free Smiling Face Detection In The Wild

We present a novel smiling face detection framework called SmileNet for detecting faces and recognising smiles in the wild. SmileNet uses a Fully Convolutional Neural Network (FCNN) to detect multiple smiling faces in a given image of varying resolution. Our contributions are threefold: 1) SmileNet...

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Bibliographic Details
Published in2017 IEEE International Conference on Computer Vision Workshops (ICCVW) pp. 1581 - 1589
Main Authors Youngkyoon Jang, Gunes, Hatice, Patras, Ioannis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Computer architecture
Convolution
Face
Face detection
Face recognition
Feature extraction
Heating systems
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Summary:We present a novel smiling face detection framework called SmileNet for detecting faces and recognising smiles in the wild. SmileNet uses a Fully Convolutional Neural Network (FCNN) to detect multiple smiling faces in a given image of varying resolution. Our contributions are threefold: 1) SmileNet is the first smiling face detection network that does not require pre-processing such as face detection and registration in advance to generate a normalised (cropped and aligned) input image; 2) the proposed SmileNet is a simple and single FCNN architecture simultaneously performing face detection and smile recognition, which are conventionally treated as separate consecutive pipelines; and 3) SmileNet ensures real-time processing speed (21:15 FPS) even when detecting multiple smiling faces in a given image (300×300). Experimental results show that SmileNet can deliver state-of-the-art performance (95:76%), even under occlusions, and variances of pose, scale, and illumination.
ISSN:2473-9944
DOI:10.1109/ICCVW.2017.186