Gender and Smile Classification Using Deep Convolutional Neural Networks

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Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach (2017, methodology): “1%, which is lower than the top-2 methods (SIAT MMLAB [59] and IVA NLPR [60]) reported in [45].”
An All-In-One Convolutional Neural Network for Face Analysis (2016, background): “Faces of the world [13] challenge dataset further advanced the research on these tasks for faces with varying scale, illumination and pose [28], [44], [53].”
Implicit Diversity in Image Summarization (2019, methodology): “If the queries come from a pre-determined set, then supervised approaches for image classification can also be used for summarization [73, 72, 95, 33].”
Improving Smiling Detection with Race and Gender Diversity (2017, background): “The models compared are [46, 21, 41, 14, 28, 42, 22, 47] from left to right in the legend.”
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity (2017, background): “…our smile model SMILING + R + G reaches an accuracy 1.62% (absolute) higher than the 2016 ChaLearn Looking at People Challenge winner SIAT MMLAB (Zhang et al., 2016), and 0.13% (absolute) higher than the published state-of-the-art UMIACS (Rajeev Ranjan & Chellappa, 2017) on the FotW validation…”
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2019, unknown): “in the smile-classification task of [68].”