Decoupling facial motion features and identity features for micro-expression recognition.

Background: Micro-expression is a kind of expression produced by people spontaneously and unconsciously when receiving stimulus. It has the characteristics of low intensity and short duration. Moreover, it cannot be controlled and disguised. Thus, micro-expression can objectively reflect people's real emotional states. Therefore, automatic recognition of micro-expressions can help machines better understand the users' emotion, which can promote human-computer interaction. What's more, micro-expression recognition has a wide range of applications in fields like security systems and psychological treatment. Nowadays, thanks to the development of artificial intelligence, most micro-expression recognition algorithms are based on deep learning. The features extracted by deep learning model from the micro-expression video sequences mainly contain facial motion feature information and identity feature information. However, in micro-expression recognition tasks, the motions of facial muscles are subtle. As a result, the recognition can be easily interfered by identity feature information. Methods: To solve the above problem, a micro-expression recognition algorithm which decouples facial motion features and identity features is proposed in this paper. A Micro-Expression Motion Information Features Extraction Network (MENet) and an Identity Information Features Extraction Network (IDNet) are designed. By adding a Diverse Attention Operation (DAO) module and constructing divergence loss function in MENet, facial motion features can be effectively extracted. Global attention operations are used in IDNet to extract identity features. A Mutual Information Neural Estimator (MINE) is utilized to decouple facial motion features and identity features, which can help the model obtain more discriminative micro-expression features. Results: Experiments on the SDU, MMEW, SAMM and CASME II datasets were conducted, which achieved competitive results and proved the superiority of the proposed algorithm.

Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms.

Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide. Therefore, they can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, however, their detection and recognition is difficult and relies heavily on expert experiences. Due to its intrinsic particularity and complexity, video-based micro-expression analysis is attractive but challenging, and has recently become an active area of research. Although there have been numerous developments in this area, thus far there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences between macro- and micro-expressions, then use these differences to guide our research survey of video-based micro-expression analysis in a cascaded structure, encompassing the neuropsychological basis, datasets, features, spotting algorithms, recognition algorithms, applications and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are addressed and discussed. Furthermore, after considering the limitations of existing micro-expression datasets, we present and release a new dataset - called micro-and-macro expression warehouse (MMEW) - containing more video samples and more labeled emotion types. We then perform a unified comparison of representative methods on CAS(ME) 2 for spotting, and on MMEW and SAMM for recognition, respectively. Finally, some potential future research directions are explored and outlined.

Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model.

Nowadays, ground-coupled heat pump system (GCHP) becomes one of the most energy-efficient systems in heating, cooling and hot water supply. However, it remains challenging to accurately predict thermal energy conversion, and the numerical calculation methods are too complicated. First, according to seasonality, this paper analyzes four variables, including the power consumption of heat pump, the power consumption of system, the ratios of the heating capacity (or the refrigerating capacity) of heat pump to the operating powers of heat pump and to the total system, respectively. Then, heat transfer performance of GCHP by historical data and working parameters is predicted by using random forests algorithm based on autoregressive model and introducing working parameters. Finally, we conduct experiments on 360-months (30-years) data generated by GCHP software. Among them, the first 300 months of data are used for training the model, and the last 60 months of data are used for prediction. Benefitting from the working condition inputs it contained, our model achieves lower Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) than Exponential Smoothing (ES), Autoregressive Model (AR), Autoregressive Moving Average Model (ARMA) and Auto-regressive Integrated Moving Average Model (ARIMA) without working condition inputs.

Coupled Patch Alignment for Matching Cross-view Gaits.

Gait recognition has attracted growing attention in recent years as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a Coupled Patch Alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest-neighbors. Then we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with Canonical Correlation Analysis (CCA). Algorithmically, we extend CPA to "Multi-dimensional Patch Alignment" (MPA) that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.

[Automatic Epileptic Electroencephalogram Detection during Normal,Interictal and Ictal Periods Combining Feature Extraction Based on Sample Entropy and Wavelet Packet Energy with Real AdaBoost Algorithm].

Electroencephalogram(EEG)analysis has been widely used in disease diagnosis.The EEG detection of the patients with epilepsy can be used to make judgments about patients' conditions in time,which is of great practical value.Therefore,the techniques of automatic detection,diagnosis and classification of epileptic EEG signals are urgently needed.In order to realize fast and accurate automatic detection and classification of the EEG signals during the normal,interictal and ictal periods of epilepsy,we propose an automatic classification and recognition method which combines the Real Adaboost algorithm based on error-correcting output codes(ECOC)with a feature extraction method based on sample entropy(SampEn)and wavelet packet energy in this paper.In the present study,we used the sample entropy of input signals and the energy of some parts of frequency bands as features,and then we classified the extracted features with the method combining ECOC with Real AdaBoost algorithm.In order to test the validity,we used the epilepsy database from the University of Bonn.The database has 5groups of EEG signals,which contains the data of normal people with their eyes open or closed,the data collected inside and outside of the epileptic foci from patients during their interictal period and the data from patients during their ictal period.The results showed that the method had strong abilities of classification and recognition of the EEG signals,and especially the recognition rate had been improved significantly.The average recognition rate of the EEG signals with different features during the three periods of the five groups mentioned above can reach 96.78%,which is superior to those with algorithms recorded in many other literatures.The method has better stability,processing speed and potential of real-time application,and it plays a supporting role in the prediction and detection of epilepsy in clinical practice.