Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.

Ensemble learning: A survey

Journal of the American Statistical Association: William S. Cleveland, Marylyn E. McGill and Robert McGill, “The shape parameter for a two variable graph” 83 (1988) 289–300

Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.

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Speech Emotion Recognition Using Deep Learning Techniques: A Review

With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.

Face detection techniques: a review

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state-of-the-art results in facial expression recognition (FER). To obtain automatic features, we experiment with multiple CNN architectures, pre-trained models, and training procedures, e.g., Dense–Sparse–Dense. After fusing the two types of features, we employ a local learning framework to predict the class label for each test image. The local learning framework is based on three steps. First, a k-nearest neighbors model is applied in order to select the nearest training samples for an input test image. Second, a one-versus-all support vector machines (SVM) classifier is trained on the selected training samples. Finally, the SVM classifier is used to predict the class label only for the test image it was trained for. Although we have used local learning in combination with handcrafted features in our previous work, to the best of our knowledge, local learning has never been employed in combination with deep features. The experiments on the 2013 FER Challenge data set, the FER+ data set, and the AffectNet data set demonstrate that our approach achieves the state-of-the-art results. With a top accuracy of 75.42% on the FER 2013, 87.76% on the FER+, 59.58% on the AffectNet eight-way classification, and 63.31% on the AffectNet seven-way classification, we surpass the state-of-the-art methods by more than 1% on all data sets.

Local Learning With Deep and Handcrafted Features for Facial Expression Recognition

Convolutional neural network (CNN) is a very effective method to recognize facial emotions. However, the preprocessing and selection of parameters of these methods heavily depend on the human experience and require a large amount of trial-and-errors. This paper presents an ensemble of convolutional neural networks method with probability-based fusion for facial expression recognition, where the architecture of CNN was adapted by using the convolutional rectified linear layer as the first layer and multiple hidden maxout layers. It was constructed by randomly varying parameters and architecture around the optimal values for CNN, where each CNN as the base classifier was trained to output a probability for each class. These probabilities were then fused through the probability-based fusion method. The conducted experiments on benchmark data sets validated our method, which had better accuracy than the compared methods. The proposed method was novel and efficient for facial expression recognition.

Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

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Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Automatic emotion recognition from speech signals is one of the important research areas. Most speech emotion recognition methods have been proposed, among which ensemble learning is an effective way. However, they are still confronted with problems, such as the curse of dimensionality and the diversity of the base classifiers hardly ensured. To overcome the problems, this paper proposes an ensemble Softmax regression model for speech emotion recognition (ESSER). It applies the feature extraction methods with much different principles to generate the subspaces for the base classifier, so that the diversity of the base classifiers could be ensured. Furthermore, a feature selection method that selects features according to global structure of the data is used to reduce the dimension of subspaces, which can further increase the diversity of the base classifiers and overcome the curse of dimensionality. As in the case of the diversity of the base classifiers ensured, the performance of ensemble classifier highly depends on the ability of the base classifier, it is reasonable for ESSER to select Softmax as the base classifier as Softmax has shown its superiority in speech emotion recognition. The conducted experiments validate the proposed approach in term of the performance of speech emotion recognition.

Ensemble softmax regression model for speech emotion recognition

A modified support vector machine and its application to image segmentation

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

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Guihua Wen

Papers

Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Ensemble softmax regression model for speech emotion recognition

A modified support vector machine and its application to image segmentation

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image