In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition. And, the third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.
We present a novel human vision inspired framework that can recognize facial expressions very efficiently and accurately. We propose to computationally process small, salient region of the face to extract features as it happens in human vision. To determine which facial region(s) is perceptually salient for a particular expression, we conducted a psycho-visual experimental study with an eye-tracker. A novel feature space conducive for recognition task is proposed, which is created by extracting Pyramid Histogram of Orientation Gradients features only from the salient facial regions. By processing only salient regions, proposed framework achieved two goals: (a) reduction in computational time for feature extraction (b) reduction in feature vector dimensionality. The proposed framework achieved automatic expression recognition accuracy of 95.3 on extended Cohn-Kanade (CK+) facial expression database for six universal facial expressions.  Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyper-parameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publicly available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of our proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time. Relatively small data sets available for expression recognition research make the training of deep networks very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redundant information from the pretrained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully-connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization results show that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu- CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.