Exploiting multi-CNN features in CNN-RNN based dimensional emotion recognition on the OMG in-the-wild dataset

This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions. Fusion of the networks is also examined for boosting the obtained performance, at Decision-, or at Model-level; in the latter case a RNN was used for the fusion. Our approach, although using only the visual modality, outperformed state-of-the-art methods that utilized audio and visual modalities. Some of our developments have been submitted to the OMG-Emotion Challenge, ranking second among the technologies which used only visual information for valence estimation; ranking third overall. Through extensive experimentation, we further show that arousal estimation is greatly improved when low-level features are combined with high-level ones

Recovering joint and individual components in facial data

A set of images depicting faces with different expressions or in various ages consists of components that are shared across all images (i.e., joint components) and imparts to the depicted object the properties of human faces and individual components that are related to different expressions or age groups. Discovering the common (joint) and individual components in facial images is crucial for applications such as facial expression transfer. The problem is rather challenging when dealing with images captured in unconstrained conditions and thus are possibly contaminated by sparse non-Gaussian errors of large magnitude (i.e., sparse gross errors) and contain missing data. In this paper, we investigate the use of a method recently introduced in statistics, the so-called Joint and Individual Variance Explained (JIVE) method, for the robust recovery of joint and individual components in visual facial data consisting of an arbitrary number of views. Since, the JIVE is not robust to sparse gross errors, we propose alternatives, which are 1) robust to sparse gross, non-Gaussian noise, 2) able to automatically find the individual components rank, and 3) can handle missing data. We demonstrate the effectiveness of the proposed methods to several computer vision applications, namely facial expression synthesis and 2D and 3D face age progression in-the-wild

Disentangling the modes of variation in unlabelled data

Statistical methods are of paramount importance in discovering the modes of variation in visual data. The Principal Component Analysis (PCA) is probably the most prominent method for extracting a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity, expression, pose etc. To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces relying on multilinear (tensor) decomposition (e.g., Higher Order SVD) have been developed. The main drawbacks of such methods is that they require both labels regarding the modes of variations and the same number of samples under all modes of variations (e.g., the same face under different expressions, poses etc.). Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. In this paper, we propose a novel general multilinear matrix decomposition method that discovers the multilinear structure of possibly incomplete sets of visual data in unsupervised setting (i.e., without the presence of labels). We also propose extensions of the method with sparsity and low-rank constraints in order to handle noisy data, captured in unconstrained conditions. Besides that, a graph-regularised variant of the method is also developed in order to exploit available geometric or label information for some modes of variations. We demonstrate the applicability of the proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the wild and with occlusion removal), expression transfer, and estimation of surface normals from images captured in the wild

Feature-based Lucas-Kanade and Active Appearance Models

Lucas-Kanade and Active Appearance Models are among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize non-linear gradient descent, which is usually applied on intensity values. In this paper, we propose the employment of highly-descriptive, densely-sampled image features for both problems. We show that the strategy of warping the multi-channel dense feature image at each iteration is more beneficial than extracting features after warping the intensity image at each iteration. Motivated by this observation, we demonstrate robust and accurate alignment and fitting performance using a variety of powerful feature descriptors. Especially with the employment of HOG and SIFT features, our method significantly outperforms the current state-of-the-art results on in-the-wild databases

Adaptive cascaded regression

Abstract The two predominant families of deformable models for the task of face alignment are: (i) discriminative cascaded regression models, and (ii) generative models optimised with Gauss-Newton. Although these approaches have been found to work well in practise, they each suffer from convergence issues. Cascaded regression has no theoretical guarantee of convergence to a local minimum and thus may fail to recover the fine details of the object. Gauss-Newton optimisation is not robust to initialisations that are far from the optimal solution. In this paper, we propose the first, to the best of our knowledge, attempt to combine the best of these two worlds under a unified model and report state-of-the-art performance on the most recent facial benchmark challenge

Time-series clustering with jointly learning deep representations, clusters and temporal boundaries

Abstract Clustering and segmentation of temporal data is an important task across several fields, with prominent applications in computer vision and machine learning such as face and gesture segmentation. Several related methods have been proposed in literature, focusing on learning temporal boundaries and clusters, with recent works focusing on learning deep representations for clustering. However, none of the proposed methods is suitable for jointly learning segments, clusters, as well as representations. In this paper, we propose the first methodology that simultaneously discovers suitable deep representations, as well as clusters and temporal boundaries, with the clustering process providing supervisory cues for updating temporal boundaries and training the proposed deep learning architecture. We demonstrate the power of the proposed approach on a human motion segmentation task using the CMU-MMAC database. Our method provides the best results with respect to normalized mutual information compared to other clustering algorithms

A comprehensive performance evaluation of deformable face tracking “In-the-wild”

Abstract Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as “in-the-wild”). This is partially attributed to the fact that comprehensive “in-the-wild” benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking “in-the-wild”. Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic