Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets--SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency.
Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.
Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.
Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to streamline the major stages in the ML lifecycle, they are normally overqualified and insufficient for an ML system in its nascent phase. Driven by real-world experience in building and maintaining ML systems, we find that it is more efficient to initialize the major stages of ML lifecycle first for trial and error, followed by the extension of specific stages to acclimatize towards more complex scenarios. For this, we introduce a simple yet flexible framework, MLife, for fast ML lifecycle initialization. This is built on the fact that data flow in MLife is in a closed loop driven by bad cases, especially those which impact ML model performance the most but also provide the most value for further ML model development-a key factor towards enabling enterprises to fast track their ML capabilities. Better yet, MLife is also flexible enough to be easily extensible to more complex scenarios for future maintenance. For this, we introduce two real-world use cases to demonstrate that MLife is particularly suitable for ML systems in their early phases.
Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual's character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving foul language owing to human weariness and the low performance in human visual systems concerning long screening time occurred. As such, this paper proposed an intelligent system for foul language censorship through a mechanized and strong detection method using advanced deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) through Long Short-Term Memory (LSTM) cells. Data on foul language were collected, annotated, augmented, and analysed for the development and evaluation of both CNN and RNN configurations. Hence, the results indicated the feasibility of the suggested systems by reporting a high volume of curse word identifications with only 2.53% to 5.92% of False Negative Rate (FNR). The proposed system outperformed state-of-the-art pre-trained neural networks on the novel foul language dataset and proved to reduce the computational cost with minimal trainable parameters.
In patients with darker skin types (Fitzpatrick phototypes III-VI), acne is often accompanied by post-inflammatory hyperpigmentation (PIH). Further, acne-related pigmentation can pose a greater concern for the patient than the acne lesions. There has been little formal study of this acne-related PIH. Recently, the Asian Acne Board - an international group of dermatologists with interest in acne research - made a preliminary evaluation of the frequency and characteristics of PIH in seven Asian countries. A total of 324 sequential acne subjects were evaluated for the presence of PIH. The majority (80.2%) of subjects had mild to moderate acne and there were more females than males (63.0% vs 37.0%). In this population of patients consulting a dermatologist for acne, 58.2% (188/324) had PIH. The results also showed that pigmentation problems are often long lasting: at least 1 year for more than half of subjects and 5 years or longer in 22.3%. In accordance with our clinical experience, patients reported that PIH is quite bothersome, often as bothersome or more so than the acne itself and sometimes more problematic. Excoriation was commonly reported by patients, and may represent a modifiable risk factor that could potentially be improved by patient education.