SmileNet: Registration-Free Smiling Face Detection in the Wild

Youngkyoon Jang, Hatice Gunes, Ioannis Patras; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1581-1589

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 (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.