An Efficient Facial Feature Extraction Method Based Supervised Classification Model for Human Facial Emotion Identification

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

In a standard k-nearest neighbor (kNN) technique, imputations of unit-level values in the variables of interest (Y) are based on the k-nearest neighbors in a set of reference units. Nearest is defined with respect to a distance metric in the space of auxiliary variables (X). This study evaluates kNN imputations of Y with a selection, by the same distance metric, of k-nearest locally weighted regression models. Imputations are obtained as predictions using the X values of the k-nearest neighbors in the population. In simulated random sampling from three artificial multivariate populations and two actual univariate populations and sampling units composed of a single population element or a cluster of four elements, the new kNN technique: (1) improved the correlation between an imputation and its actual value; (2) lowered the root mean square error (RMSE) of imputations; (3) increased the slope in regressions of actual y values regressed against their imputed values; (4) performed relatively best with k values of 4 and sample sizes of 200 or greater; (5) compared favorably with a recently proposed kNN calibration procedure; and (6) had a higher (15–28%) RMSE than with a simple local linear regression. Distribution matching had a consistent negative effect (+10%) on RMSE.

This paper proposes and evaluates the use of machine learning (ML) techniques for mitigating the effect of the random inter-core crosstalk (ICXT) on 256 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers–Kronig (KK) receivers. The performance improvement provided by the k-means clustering, k nearest neighbor (KNN) and feedforward neural network (FNN) techniques are assessed and compared with the system performance obtained without employing ML. The FNN proves to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features for the training phase. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew×symbol rate≪1). By employing the proposed FNN, the bit error rate (BER) always stood below 10−1.8 on all the time fractions under analysis (compared with 100 out of 626 occurrences above the BER threshold when ML was not used). For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed FNN shows a received optical power improvement of almost 3 dB.

Facial expressions deliver rich information about human emotion and play an essential role in human communications. This paper presents a design and evaluation of a novel computational model that categorizes facial expressions in real time video for the reason of automating human computer interfaces. It highlights the main system components, methodology for the development of the prototype and some research challenges. The concepts of correlation have been used to detect the face in video sequences and multiclass SVM is used for classification. The method has been evaluated in terms of recognition accuracy using a well known Facial Expression database, Japanese Female Facial Expression database as well as using the database of face images of the authors. The experimental results show the effectiveness of our scheme.

Facial Expression Recognition (FER) is a critical component of affective computing, with deep learning models dominating performance metrics. In contrast, geometric approaches based on the Facial Action Coding System (FACS) offer explainability through using triangles aligned to facial landmarks. The notable points of these triangles capture the deformation of muscles. However, restricting the feature extraction to notable points may be suboptimal. This paper introduces a novel method for optimizing the extraction of features by searching for optimal inner points in 22 facial triangles applying three metaheuristics: Differential Evolution (DE), Particle Swarm Optimization (PSO), and Convex Partition (CP). This results in a set of 59 geometric-based descriptors that capture muscle deformation more accurately. The proposed method was evaluated using five machine learning classifiers on two benchmark databases: the Karolinska Directed Emotional Faces (KDEF) and the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate significant performance improvements. The combination of DE with a Multi-Layer Perceptron (MLP) achieved an accuracy of 0.91 on the KDEF database, while Support Vector Machine (SVM) optimized via CP attained an accuracy of 0.81 on the JAFFE database. Statistical analysis confirms that optimized descriptors yield higher accuracy than previous geometric methods.

Abstract:Mathematical decision-making abilities are mathematical information processing through risk evaluation and investigation of various possibilities and perspectives. However, the evaluation of mathematical decision-making abilities is still limited to high, medium, and a low level based on test scores and is not predictive naturally. The purpose of this study is to identify the parameter k in the k-nearest neighbor technique, which serves as the nearest-neighbor value determining the mathematical decision-making abilities of students to be predicted. The process of data exploration to prediction is employed by the data mining approach with the k-Nearest Neighbor method. A total of 65 first-year students taking Calculus I included as research samples. The research results show that a parameter value of k=15 is better at predicting the closeness of the mathematical decision-making with an accuracy of 93.33%, associated with the excellent category. The parameter value representing the closeness of the decision-making abilities level among students serves as a reference for teacher predictions to categorize students and create diversified teaching materials.Abstrak:Kemampuan pengambilan keputusan matematis merupakan pemrosesan informasi secara matematis melalui evaluasi risiko dan penyelidikan berbagai kemungkinan dan sudut pandang. Namun, evaluasi kemampuan pengambilan keputusan matematis masih terbatas pada level tinggi, sedang dan rendah berdasarkan nilai tes, tidak bersifat prediktif. Penelitian ini bertujuan mengidentifikasi nilai parameter k dengan teknik k-nearest neighbor yang berperan sebagai nilai ketetanggaan terdekat dari kemampuan pengambilan keputusan matematis. Proses eksplorasi data hingga prediksi dilakukan menggunakan pendekatan data mining dengan metode k-Nearest Neighbor. Sebanyak 65 mahasiswa tahun pertama yang mengikuti Kalkulus I sebagai sampel penelitian. Hasil penelitian menunjukkan nilai parameter k=15 yang lebih mampu memprediksi kedekatan tingkat kemampuan pengambilan keputusan matematis dengan akurasi 93,33%, termasuk pada kategori excellent. Nilai parameter tersebut menjadi acuan prediksi pengajar untuk mengkategorikan mahasiswa dan membuat diversifikasi bahan ajar.

Speech emotion recognition is a challenging task for three main reasons: 1) human emotion is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be detected in some specific moments during a long utterance; 3) speech data with emotional labeling is usually limited. In this paper, we present a novel attention based fully convolutional network for speech emotion recognition. We employ fully convolutional network as it is able to handle variable-length speech, free of the demand of segmentation to keep critical information not lost. The proposed attention mechanism can make our model be aware of which time-frequency region of speech spectrogram is more emotion-relevant. Considering limited data, the transfer learning is also adapted to improve the accuracy. Especially, it's interesting to observe obvious improvement obtained with natural scene image based pre-trained model. Validated on the publicly available IEMOCAP corpus, the proposed model outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and an unweighted accuracy of 63.9% respectively.

We propose an efficient algorithm for recognizing facial expressions using biologically plausible features: contours of face and its components with radial encoding strategy. A self-organizing network (SON) is applied to check the homogeneity of the encoded contours and then different classifiers, such as SON, multi-layer perceptron and K-nearest neighbor, are used for recognizing expressions from contours. Experimental results show that the recognition accuracy of our algorithm is comparable to that of other algorithms in the literature on the Japanese female facial expression database. We also apply our algorithm to Taiwanese facial expression image database to demonstrate its efficiency in recognizing facial expressions.

This research aims to predict succinic acid concentration in the external phase during the emulsion liquid membrane process by using artificial neural networks along with a popular alternative method: k-nearest neighbor technique. The solute concentration values can be predicted by the proposed method without performing a great number of emulsion liquid membrane experiments. Several computer simulations were performed to demonstrate the success of the system. Simulation results showed that the estimated solute concentration values are very close to the achieved experimental results. The optimal conditions for emulsion liquid membrane were found to be: solvent kerosene, TOPO concentration (1%w/w), Amberlite LA-2 concentration (4%w/w), surfactant concentration (5%w/w), Na2CO3 concentration (5%w/v), modifier (decanol) concentration (2%w/w), mixing speed 300 rpm. The average accuracy percentages achieved by artificial neural network and k-nearest neighbor approaches were 88.75and#177;1.94% and 90.2and#177;1.2%, respectively.

Facial expression recognition (FER) based on a new nonlinear feature extraction method, called kernel discriminant plane (KDP), is proposed in this paper. KDP is a nonlinear extension of the Sammon’s optimal discriminant plane (ODP) via the kernel trick. The recognition procedure is divided into two steps: (1) we select 34 fiducial points manually from each facial image and use the coordinates of these points as the input data of the facial image; (2) we construct a multiple binary classifier for the classification purpose of this task. The better performance of the proposed method is confirmed by the Japanese Female Facial Expression (JAFFE) database.

Texture characteristics among the landmark points reflected in human faces are important features in the recognition of facial expression. In this paper, we propose a salient feature based facial expression on texture characteristics. Detection of effective landmarks are performed by appearance based models and corresponding texture regions are extracted from face images. Local Binary Pattern (LBP) is used to compute the texture feature. Texture feature is used for normalizing texture signatures. In addition, we introduce stability indices corresponding to texture features which are exploited as features in the present scope. Moreover, statistical features such as moment, skewness, kurtosis and entropy are also supplemented to the feature set. These salient features together are used as an input to Nonlinear AutoRegressive with eXogenous (NARX) for recognition of the human facial expression. The Cohn-Kanade (CK+), Japanese Female Facial Expression (JAFFE), MMI and MUG benchmark databases are used to conduct the experiments and the results justify the effectiveness of the proposed procedure.

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

Recognition of human faces is a very important task in many applications such as authentication and surveillance. An efficient face recognition system with face image representation using averaged wavelet and wavelet packet coefficients, Discriminative Common Vector (DCV) and modified Local Binary Patterns (LBP) and recognition using radial basis function (RBF) network is presented. Face images are decomposed by 2-level wavelet and wavelet packet transformation. The discriminative common vectors are obtained for averaged wavelet. The new proposed LBP operator is applied on the obtained DCV and also applied on averaged wavelet packet coefficients of all the samples of a class. The histogram values obtained from the LBP are recognized using RBF network. The proposed work is tested on three face databases such as Olivetti Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The proposed method results in good recognition rates along with less training time because of the extracted discriminant input from the preprocessing steps involved in the proposed work.

Facial expressions are important in people’s daily communications. Recognising facial expressions also has many important applications in the areas such as healthcare and e-learning. Existing facial expression recognition systems have problems such as background interference. Furthermore, systems using traditional approaches like SVM (Support Vector Machine) have weakness in dealing with unseen images. Systems using deep neural network have problems such as requirement for GPU, longer training time and requirement for large memory. To overcome the shortcomings of pure deep neural network and traditional facial recognition approaches, this paper presents a new facial expression recognition approach which has image pre-processing techniques to remove unnecessary background information and combines deep neural network ResNet50 and a traditional classifier-- the multiclass model for Support Vector Machine to recognise facial expressions. The proposed approach has better recognition accuracy than traditional approaches like Support Vector Machine and doesn’t need GPU. We have compared 3 proposed frameworks with a traditional SVM approach against the Karolinska Directed Emotional Faces (KDEF) Database, the Japanese Female Facial Expression (JAFFE) Database and the extended Cohn-Kanade dataset (CK+), respectively. The experiment results show that the features extracted from the layer 49Relu have the best performance for these three datasets.

In this paper, we proposed a novel algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Local Phase Quantization (LPQ). Firstly, the LPQ feature and gabor texture feature were respectively extracted from every expression image. LPQ features are histograms of LPQ transform. Five scales and eight orientations of gabor wavelet filters are used to extract gabor texture features and adaboost algorithm is used to select gabor features. Then we obtain two expression recognition results on both expression features by Sparse Representation-based Classification (SRC) method. Finally, the final expression recognition was performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate than the traditional algorithm.