SmileNet: Registration-Free Smiling Face Detection in the Wild
Proceedings of the IEEE International Conference on Computer Vision
ICCVW 2017 IEEE International Conference on Computer Vision Workshop
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Jang, Y., Gunes, H., & Patras, I. (2017). SmileNet: Registration-Free Smiling Face Detection in the Wild. Proceedings of the IEEE International Conference on Computer Vision https://doi.org/10.17863/CAM.13769
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 three-fold: 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 (300x300). Experimental results show that SmileNet can deliver state-of-the-art performance (95.76%), even under occlusions, and variances of pose, scale, and illumination.
This work is supported by the Technology Strategy Board / Innovate UK project Sensing Feeling (project no. 102547).
This record's DOI: https://doi.org/10.17863/CAM.13769
This record's URL: https://www.repository.cam.ac.uk/handle/1810/267848