The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.
Common binary classifiers applied to this problem include Artificial Neural Networks (ANN), Boosting techniques, and Support Vector Machines (SVM). ANNs were the most popular method in earlier works (e.g. @cite_8 , @cite_31 ). Boosting algorithms, such as AdaBoost and GentleBoost, have been a common choice for AU recognition (e.g. @cite_3 , @cite_26 ). Boosting algorithms are simple and quick to train. They have fewer parameters than SVM or ANN, and can be less prone to overfitting. They implicitly perform feature selection, which is desirable for handling high-dimensional data and speeding up inference, and can handle multiclass classification. SVMs are currently the most popular choice (e.g. @cite_15 , @cite_5 , @cite_1 ). SVMs provide good performance, can be non-linear, parameter optimisation is relatively easy, as efficient implementations are readily available, and a choice of kernel functions provides extreme flexibility of design.
@cite_26 @cite_15 @cite_8 @cite_1 @cite_3 @cite_5 @cite_31
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We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM’s lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties. Two-category support vector machines (SVM) have been very popular in the machine learning community for classification problems. Solving multicategory problems by a series of binary classifiers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when there are either equal or unequal misclassification costs. As a tuning criterion for the MSVM, an approximate leave-one-out cross-validation function, called Generalized Approximate Cross Validation, is derived, analogous to the binary case. The effectiveness of the MSVM is demonstrated through the applications to cancer classification using microarray data and cloud classification with satellite radiance profiles. Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech recognition tasks. For example, the models can make use of additional dependencies in the observation sequences than HMMs provided the appropriate form of kernel is used. However standard SVMs are binary classifiers, and speech is a multi-class problem. Furthermore, to train SVMs to distinguish word pairs requires that each word appears in the training data. This paper examines both of these limitations. Tree-based reduction approaches for multiclass classification are described, as well as some of the issues in applying them to dynamic data, such as speech. To address the training data issues, a simplified version of HMM-based synthesis can be used, which allows data for any word-pair to be generated. These approaches are evaluated on two noise corrupted digit sequence tasks: AURORA 2.0; and actual in-car collected data. Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.