Facial Expression Recognition from World Wild Web

Abstract

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.

References in corpus (6)

• Going Deeper with Convolutions
• Going Deeper in Facial Expression Recognition using Deep Neural Networks
• Training Convolutional Networks with Noisy Labels
• ExpressionBot: An Emotive Lifelike Robotic Face for Face-to-Face Communication
• Bidirectional Warping of Active Appearance Model
• eBear: An Expressive Bear-Like Robot

Cited by in corpus (13)

• AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
• Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
• Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks
• Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields
• BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient
• DAiSEE: Towards User Engagement Recognition in the Wild
• Facial Affect Estimation in the Wild Using Deep Residual and Convolutional Networks
• CNN-based Facial Affect Analysis on Mobile Devices
• Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing
• Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
• Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
• Disentanglement for Discriminative Visual Recognition
• An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild