Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields

Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorous hyperparameter tuning to achieve good results. Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition. In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos. The first part captures the spatial relation within facial images using convolutional layers followed by three Inception-ResNet modules and two fully-connected layers. To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network. We evaluate our proposed network on three publicly available databases, viz. CK+, MMI, and FERA. Experiments are performed in subject-independent and cross-database manners. Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the state-of-the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.Comment: To appear in 12th IEEE Conference on Automatic Face and Gesture Recognition Worksho

Analysing Qualitative Data Using Facial Expressions in an Educational Scenario

In communication, both verbal and non-verbal means ensure that a message is conveyed, and facial expressions are acknowledged as one of the most influential factors in non-verbal communication. Facial Analysis Coding System (FACS) is a tool to analyse data other than the spoken language to improve a researcher's reading of an interviewee's emotions, and proposes a methodology to support the annotation process of facial expressions in a piece of communication. This study investigates an applied framework for FACS in an educational scenario. The study combines both the computerised and manual entries in the applied method. The study addresses the challenges, findings and recommendations of this applied method

Deep learning the dynamic appearance and shape of facial action units

Spontaneous facial expression recognition under uncontrolled conditions is a hard task. It depends on multiple factors including shape, appearance and dynamics of the facial features, all of which are adversely affected by environmental noise and low intensity signals typical of such conditions. In this work, we present a novel approach to Facial Action Unit detection using a combination of Convolutional and Bi-directional Long Short-Term Memory Neural Networks (CNN-BLSTM), which jointly learns shape, appearance and dynamics in a deep learning manner. In addition, we introduce a novel way to encode shape features using binary image masks computed from the locations of facial landmarks. We show that the combination of dynamic CNN features and Bi-directional Long Short-Term Memory excels at modelling the temporal information. We thoroughly evaluate the contributions of each component in our system and show that it achieves state-of-the-art performance on the FERA-2015 Challenge dataset

Deep learning the dynamic appearance and shape of facial action units

Spontaneous facial expression recognition under uncontrolled conditions is a hard task. It depends on multiple factors including shape, appearance and dynamics of the facial features, all of which are adversely affected by environmental noise and low intensity signals typical of such conditions. In this work, we present a novel approach to Facial Action Unit detection using a combination of Convolutional and Bi-directional Long Short-Term Memory Neural Networks (CNN-BLSTM), which jointly learns shape, appearance and dynamics in a deep learning manner. In addition, we introduce a novel way to encode shape features using binary image masks computed from the locations of facial landmarks. We show that the combination of dynamic CNN features and Bi-directional Long Short-Term Memory excels at modelling the temporal information. We thoroughly evaluate the contributions of each component in our system and show that it achieves state-of-the-art performance on the FERA-2015 Challenge dataset

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy1, and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.Comment: ICCV 2017 - accepte