Underwater exploration has become an active research area over the past few decades. The image enhancement is one of the challenges for those computer vision based underwater researches because of the degradation of the images in the underwater environment. The scattering and absorption are the main causes in the underwater environment to make the images decrease their visibility, for example, blurry, low contrast, and reducing visual ranges. To tackle aforementioned problems, this paper presents a novel method for underwater image enhancement inspired by the Retinex framework, which simulates the human visual system. The term Retinex is created by the combinations of Retina and Cortex. The proposed method, namely LAB-MSR, is achieved by modifying the original Retinex algorithm. It utilizes the combination of the bilateral filter and trilateral filter on the three channels of the image in CIELAB color space according to the characteristics of each channel. With real world data, experiments are carried out to demonstrate both the degradation characteristics of the underwater images in different turbidities, and the competitive performance of the proposed method.

The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs algorithm, called random Shapley forests (RSFs), based on the Shapley value. The Shapley value is one of the well-known solutions in the cooperative game, which can fairly assess the power of each player in a game. In the construction of RSFs, RSFs use the Shapley value to evaluate the importance of each feature at each tree node by computing the dependency among the possible feature coalitions. In particular, inspired by the existing consistency theory, we have proved the consistency of the proposed RFs algorithm. Moreover, to verify the effectiveness of the proposed algorithm, experiments on eight UCI benchmark datasets and four real-world datasets have been conducted. The results show that RSFs perform better than or at least comparable with the existing consistent RFs, the original RFs, and a classic classifier, support vector machines.

Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.

Facial paralysis refers to a facial nerve disordering, with which people may lose the abilities to accurately control their facial muscles for certain facial performances. The diagnosis of such disordering is mainly based on the observation of patient's face in terms of the facial spatial information, such as facial asymmetry. Up to now, this area is still dominated by therapists' subjective examinations clinically. Therefore, automations for this task receive wide attentions in both academic and industrial fields. Recently, the deep learning based methods, the convolutional neural networks (CNNs) more specifically, demonstrate their competitive performance compared with traditional approaches in many areas. However, due to the lack of the structured/labelled facial paralysis data as training data, those deep learning based solutions are still not able to fully attach their superiorities to the facial paralysis evaluation tasks. Another essential aspect for automation in facial paralysis analysis is the facial spatial information extraction. Semantic segmentation is a better choice than traditional template-based facial landmark detection for analysing facial paralysis images, which contain faces in uncommon patterns. However, most existing semantic segmentation approaches are made for indoor or outdoor scene parsing. To this end, this paper presents a deep learning-based approach for automatic facial paralysis grading prediction. The proposed model utilizes a cascaded encoder structure, which explores the advantages of the facial semantic feature for facial spatial information extraction, and then benefits the facial paralysis assessment. A dual-stage cascaded training process is adopted to utilize a mixture of normal and paralysed faces as training data, which exports a well-trained deep neural network model for facial paralysis evaluation. Experiments are conducted in two aspects to demonstrate the performance of each components of the proposed model. Encouraging results are illustrated compared with several existing approaches in the related areas.

Recent DeepFake detection methods have shown excellent performance on public datasets but are significantly degraded on new forgeries. Solving this problem is important, as new forgeries emerge daily with the continuously evolving generative techniques. Many efforts have been made for this issue by seeking the commonly existing traces empirically on data level. In this paper, we rethink this problem and propose a new solution from the unsupervised domain adaptation perspective. Our solution, called DomainForensics , aims to transfer the forgery knowledge from known forgeries (fully labeled source domain) to new forgeries (label-free target domain). Unlike recent efforts, our solution does not focus on data view but on learning strategies of DeepFake detectors to capture the knowledge of new forgeries through the alignment of domain discrepancies. In particular, unlike the general domain adaptation methods which consider the knowledge transfer in the semantic class category, thus having limited application, our approach captures the subtle forgery traces. We describe a new bidirectional adaptation strategy dedicated to capturing the forgery knowledge across domains. Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation. In forward adaptation, we perform supervised training for the DeepFake detector in the source domain and jointly employ adversarial feature adaptation to transfer the ability to detect manipulated faces from known forgeries to new forgeries. In backward adaptation, we further improve the knowledge transfer by coupling adversarial adaptation with self-distillation on new forgeries. This enables the detector to expose new forgery features from unlabeled data and avoid forgetting the known knowledge of known forgery. Extensive experiments demonstrate that our method is surprisingly effective in exposing new forgeries, and can be plug-and-play on other DeepFake detection architectures.