Charles E. Hughes

Weijieying Ren, Pengyang Wang, Xiaolin Li et al.

In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical streams is limited and model updating can only be achieved based on a very short lookback window. This learning setting limits the applicability and availability of many Machine Learning (ML) algorithms. We generalize the learning task under such setting as a semi-supervised drifted stream learning with short lookback problem (SDSL). SDSL imposes two under-addressed challenges on existing methods in semi-supervised learning, continuous learning, and domain adaptation: 1) robust pseudo-labeling under gradual shifts and 2) anti-forgetting adaptation with short lookback. To tackle these challenges, we propose a principled and generic generation-replay framework to solve SDSL. The framework is able to accomplish: 1) robust pseudo-labeling in the generation step; 2) anti-forgetting adaption in the replay step. To achieve robust pseudo-labeling, we develop a novel pseudo-label classification model to leverage supervised knowledge of previously labeled data, unsupervised knowledge of new data, and, structure knowledge of invariant label semantics. To achieve adaptive anti-forgetting model replay, we propose to view the anti-forgetting adaptation task as a flat region search problem. We propose a novel minimax game-based replay objective function to solve the flat region search problem and develop an effective optimization solver. Finally, we present extensive experiments to demonstrate our framework can effectively address the task of anti-forgetting learning in drifted streams with short lookback.

Dongjie Wang, Pengyang Wang, Yanjie Fu et al.

Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users' mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

Kamran Ali, Charles E. Hughes

The development of transformer-based models has resulted in significant advances in addressing various vision and NLP-based research challenges. However, the progress made in transformer-based methods has not been effectively applied to biosensing research. This paper presents a novel Unified Biosensor-Vision Multi-modal Transformer-based (UBVMT) method to classify emotions in an arousal-valence space by combining a 2D representation of an ECG/PPG signal with the face information. To achieve this goal, we first investigate and compare the unimodal emotion recognition performance of three image-based representations of the ECG/PPG signal. We then present our UBVMT network which is trained to perform emotion recognition by combining the 2D image-based representation of the ECG/PPG signal and the facial expression features. Our unified transformer model consists of homogeneous transformer blocks that take as an input the 2D representation of the ECG/PPG signal and the corresponding face frame for emotion representation learning with minimal modality-specific design. Our UBVMT model is trained by reconstructing masked patches of video frames and 2D images of ECG/PPG signals, and contrastive modeling to align face and ECG/PPG data. Extensive experiments on the MAHNOB-HCI and DEAP datasets show that our Unified UBVMT-based model produces comparable results to the state-of-the-art techniques.

Dongjie Wang, Pengyang Wang, Kunpeng Liu et al.

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, which produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile.

Kamran Ali, Charles E. Hughes

In this paper, we present an Attention-based Identity Preserving Generative Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a source image to a generated face image, an issue that is encountered in a cross-subject facial expression transfer and synthesis process. Our key insight is that the identity preserving network should be able to disentangle and compose shape, appearance, and expression information for efficient facial expression transfer and synthesis. Specifically, the expression encoder of our AIP-GAN disentangles the expression information from the input source image by predicting its facial landmarks using our supervised spatial and channel-wise attention module. Similarly, the disentangled expression-agnostic identity features are extracted from the input target image by inferring its combined intrinsic-shape and appearance image employing our self-supervised spatial and channel-wise attention mod-ule. To leverage the expression and identity information encoded by the intermediate layers of both of our encoders, we combine these features with the features learned by the intermediate layers of our decoder using a cross-encoder bilinear pooling operation. Experimental results show the promising performance of our AIP-GAN based technique.

Kamran Ali, Charles E. Hughes

Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) which combines the concept of disentangled representation learning with residue learning to explicitly disentangle facial expression representation from identity information. In this method the facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. Unlike previous works using only expression residual learning for facial expression recognition, our method learns the disentangled expression representation along with the expressive component recorded by the encoder of DE-GAN. In order to improve the quality of synthesized expression images and the effectiveness of the learned disentangled expression representation, expression and identity classification is performed by the discriminator of DE-GAN. Experiments performed on widely used datasets (CK+, MMI, Oulu-CASIA) show that the proposed technique produces comparable or better results than state-of-the-art methods.

Kamran Ali, Charles E. Hughes

In this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial expressions from one identity to another identity, known as Facial Expression Transfer (FET), 2. to transform the expression of a given image to a target expression, while preserving the identity of the image, known as Facial Expression Editing (FEE), and 3. to recognize the facial expression of a face image, known as Facial Expression Recognition (FER). In TER-GAN, we combine the capabilities of generative models to generate synthetic images, while learning important information about the input images during the reconstruction process. More specifically, two encoders are used in TER-GAN to encode identity and expression information from two input images, and a synthetic expression image is generated by the decoder part of TER-GAN. To improve the feature disentanglement and extraction process, we also introduce a novel expression consistency loss and an identity consistency loss which exploit extra expression and identity information from generated images. Experimental results show that the proposed method can be used for efficient facial expression transfer, facial expression editing and facial expression recognition. In order to evaluate the proposed technique and to compare our results with state-of-the-art methods, we have used the Oulu-CASIA dataset for our experiments.

Kamran Ali, Ilkin Isler, Charles Hughes

Facial expression recognition is a challenging task due to two major problems: the presence of inter-subject variations in facial expression recognition dataset and impure expressions posed by human subjects. In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to overcome these two problems by using many (human faces) to one (animated face) mapping. Specifically, for any given input human expression image, our HA-GAN transfers the expression information from the input image to a fixed animated identity. Stylized animated characters from the Facial Expression Research Group-Database (FERGDB) are used for the generation of fixed identity. By learning this many-to-one identity mapping function using our proposed HA-GAN, the effect of inter-subject variations can be reduced in Facial Expression Recognition(FER). We also argue that the expressions in the generated animated images are pure expressions and since FER is performed on these generated images, the performance of facial expression recognition is improved. Our initial experimental results on the state-of-the-art datasets show that facial expression recognition carried out on the generated animated images using our HA-GAN framework outperforms the baseline deep neural network and produces comparable or even better results than the state-of-the-art methods for facial expression recognition.

Kamran Ali, Charles E. Hughes

The representation used for Facial Expression Recognition (FER) usually contain expression information along with other variations such as identity and illumination. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) to explicitly disentangle facial expression representation from identity information. In this learning by reconstruction method, facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN. The process of expression image reconstruction and disentangled expression representation learning is improved by performing expression and identity classification in the discriminator of DE-GAN. The disentangled facial expression representation is then used for facial expression recognition employing simple classifiers like SVM or MLP. The experiments are performed on publicly available and widely used face expression databases (CK+, MMI, Oulu-CASIA). The experimental results show that the proposed technique produces comparable results with state-of-the-art methods.

Felix G. Hamza-Lup, Jannick P. Rolland, Charles Hughes

Augmented Reality (AR) systems describe the class of systems that use computers to overlay virtual information on the real world. AR environments allow the development of promising tools in several application domains. In medical training and simulation the learning potential of AR is significantly amplified by the capability of the system to present 3D medical models in real-time at remote locations. Furthermore the simulation applicability is broadened by the use of real-time deformable medical models. This work presents a distributed medical training prototype designed to train medical practitioners' hand-eye coordination when performing endotracheal intubations. The system we present accomplishes this task with the help of AR paradigms. An extension of this prototype to medical simulations by employing deformable medical models is possible. The shared state maintenance of the collaborative AR environment is assured through a novel adaptive synchronization algorithm (ASA) that increases the sense of presence among participants and facilitates their interactivity in spite of infrastructure delays. The system will allow paramedics, pre-hospital personnel, and students to practice their skills without touching a real patient and will provide them with the visual feedback they could not otherwise obtain. Such a distributed AR training tool has the potential to: allow an instructor to simultaneously train local and remotely located students and, allow students to actually "see" the internal anatomy and therefore better understand their actions on a human patient simulator (HPS).

Behnaz Nojavanasghari, Charles. E. Hughes, Tadas Baltrusaitis et al.

A person's face discloses important information about their affective state. Although there has been extensive research on recognition of facial expressions, the performance of existing approaches is challenged by facial occlusions. Facial occlusions are often treated as noise and discarded in recognition of affective states. However, hand over face occlusions can provide additional information for recognition of some affective states such as curiosity, frustration and boredom. One of the reasons that this problem has not gained attention is the lack of naturalistic occluded faces that contain hand over face occlusions as well as other types of occlusions. Traditional approaches for obtaining affective data are time demanding and expensive, which limits researchers in affective computing to work on small datasets. This limitation affects the generalizability of models and deprives researchers from taking advantage of recent advances in deep learning that have shown great success in many fields but require large volumes of data. In this paper, we first introduce a novel framework for synthesizing naturalistic facial occlusions from an initial dataset of non-occluded faces and separate images of hands, reducing the costly process of data collection and annotation. We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects. Finally, we present a model to localize hand over face occlusions and identify the occluded regions of the face.