Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.Comment: Accepted in AAAI-1

EmoNets: Multimodal deep learning approaches for emotion recognition in video

The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based "bag-of-mouths" model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67% on the 2014 dataset

Ensemble of Hankel Matrices for Face Emotion Recognition

In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text overlap with arXiv:1506.0500

"Mental Rotation" by Optimizing Transforming Distance

The human visual system is able to recognize objects despite transformations that can drastically alter their appearance. To this end, much effort has been devoted to the invariance properties of recognition systems. Invariance can be engineered (e.g. convolutional nets), or learned from data explicitly (e.g. temporal coherence) or implicitly (e.g. by data augmentation). One idea that has not, to date, been explored is the integration of latent variables which permit a search over a learned space of transformations. Motivated by evidence that people mentally simulate transformations in space while comparing examples, so-called "mental rotation", we propose a transforming distance. Here, a trained relational model actively transforms pairs of examples so that they are maximally similar in some feature space yet respect the learned transformational constraints. We apply our method to nearest-neighbour problems on the Toronto Face Database and NORB

Automatic Kinship Verification in Unconstrained Faces using Deep Learning

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. Identifying kinship relations has also garnered interest due to several potential applications in security and surveillance and organizing and tagging the enormous number of videos being uploaded on the Internet. This dissertation has a five-fold contribution where first, a study is conducted to gain insight into the kinship verification process used by humans. Besides this, two separate deep learning based methods are proposed to solve kinship verification in images and videos. Other contributions of this research include interlinking face verification with kinship verification and creation of two kinship databases to facilitate research in this field. WVU Kinship Database is created which consists of multiple images per subject to facilitate kinship verification research. Next, kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250,000 still frames. In this dissertation, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determines their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender, age, and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d′, and perceptual information entropy. Next, utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as the output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. The results show that the proposed deep learning framework (KVRL-fcDBN) yields state-of-the-art kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Additionally, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields videobased kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases. Finally, we end by discussing the connections between face verification and kinship verification research. We explore the area of self-kinship which is age-invariant face recognition. Further, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification. By addressing several problems of limited samples per kinship dataset, introducing real-world variations in unconstrained databases and designing two deep learning frameworks, this dissertation improves the understanding of kinship verification across humans and the performance of automated systems. The algorithms proposed in this research have been shown to outperform existing algorithms across six different kinship databases and has till date the best reported results in this field

Spatio-Temporal Relation and Attention Learning for Facial Action Unit Detection

Spatio-temporal relations among facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. The main reasons are the limited capability of current AU detection works in simultaneously learning spatial and temporal relations, and the lack of precise localization information for AU feature learning. To tackle these limitations, we propose a novel spatio-temporal relation and attention learning framework for AU detection. Specifically, we introduce a spatio-temporal graph convolutional network to capture both spatial and temporal relations from dynamic AUs, in which the AU relations are formulated as a spatio-temporal graph with adaptively learned instead of predefined edge weights. Moreover, the learning of spatio-temporal relations among AUs requires individual AU features. Considering the dynamism and shape irregularity of AUs, we propose an attention regularization method to adaptively learn regional attentions that capture highly relevant regions and suppress irrelevant regions so as to extract a complete feature for each AU. Extensive experiments show that our approach achieves substantial improvements over the state-of-the-art AU detection methods on BP4D and especially DISFA benchmarks