Abstract. Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial landmark points are detected and tracked over successive video frames using a robust method based on subspace regularization, Kalman prediction and refinement. The trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are organized in a spatiotemporal matrix and discriminative features are extracted from the trajectory matrix. Alternatively, appearance based features are extracted from DCT coefficients of several face patches. Finally Adaboost algorithm is performed to learn a set of discriminating spatiotemporal DCT features for face and head gesture (FHG) classification. We report the classification results obtained by using the Support Vector Machines (SVM) on the outputs of the features learned by Adaboost. We achieve 94.04 % subject 1 2 independent classification performance over seven FHG. 1

Abstract — Current approaches to facial expression classification employ a variety of expression classes and different preprocessing steps, making comparison of results difficult. To outline the effects of these variations we explore several image and action preprocessing steps, using the discrete expressions: happy, sad, surprised, fearful, angry, disgusted and neutral; with a dataset aligned and normalised by our proposed face model. Each of the preprocessing steps is organised across four prominent approaches: holistic, holistic action, component and component action. These are compared using a modified multiclass Support Vector Machine (SVM) that uses pairwise adaptive model parameters. We illustrate that including the neutral expression as part of the study has a noticeable impact, and suggest that it should be used in future research in this area. We also show that results can be improved through innovative use of image and action preprocessing steps. Our best correct classification rate was 98.33 % using 10-fold cross validation and a component action approach. I.

Abstract. We consider two novel representations and feature extraction schemes for automatic recognition of emotion related facial expressions. In one scheme facial landmark points are tracked over successive video frames using an effective detector and tracker to extract landmark trajectories. Features are extracted from landmark trajectories using Independent Component Analysis (ICA) method. In the alternative scheme, the evolution of the emotion expression on the face is captured by stacking normalized and aligned faces into a spatiotemporal face cube. Emotion descriptors are then 3D Discrete Cosine Transform (DCT) features from this prism or DCT & ICA features. Several classifier configurations are used and their performance determined in detecting the 6 basic emotions. Decision fusion applied to classifiers improved the recognition performance of best classifier by 9 percentage points. The proposed method was evaluated user independently on the Cohn-Kanade facial expression database and a state-of-the-art 95.34 % recognition performance is achieved. Key words: facial expression analysis, spatiotemporal features, face prism 1

Mirror my emotions! Combining facial expression analysis and synthesis on a robot

This paper presents a novel method for facial expression classification that employs the combination of two different feature sets in an ensemble approach. A pool of base classifiers is created using two feature sets: Gabor filters and local binary patterns (LBP). Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the accuracy and the size of the ensemble. The experimental results on two databases have shown the efficiency of the proposed strategy by finding powerful ensembles, which improves the recognition rates between 5 % and 10%. Index Terms — Face recognition, Emotion recognition. 1.

Abstract Human eyes are highly efficient devices for scanning through a large quantity of low-level visual sensory data and delivering selective information to one’s brain for high-level semantic interpretation and gaining situational awareness. Over the last few decades, the computer vision community has endeavoured to bring about similar perceptual capabilities to artificial visual sensors. Substantial efforts have been made towards understanding static images of individual objects and the corresponding processes in the human visual system. This endeavour is intensified further by the need for understanding a massive quantity of video data, with the aim to comprehend multiple entities not only within a single image but also over time across multiple video frames for understanding their spatio-temporal relations. A significant application of video analysis and understanding is intelligent surveillance, which aims to interpret automatically human activity and detect unusual events that could pose a threat to public security and safety. 1

Spatial Gabor energy filters (GE) are one of the most successful approaches to represent facial expressions in computer vision applications, including face recognition and expression analysis. It is well known that these filters approximate the response of complex cells in primary visual cortex. However these neurons are modulated by the temporal, not just spatial, properties of the visual signal. This suggests that spatio-temporal Gabor filters may provide useful representations for applications that involve video sequences. In this paper we explore Gabor motion energy filters (GME) as a biologically inspired representation for dynamic facial expressions. Experiments on the Cohn-Kanade expression dataset show that GME outperforms GE, particularly on difficult low intensity expression discrimination. 1.

A wide range of pattern recognition applications have been based on the Local Binary Pattern (LBP) representation of textures, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other Binary Pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. The performance advantage of the FBP-based approaches has been also validated by unsupervised segmentation of natural scenes.

Early detection and removal of pulmonary nodules significantly improves long term survival rates for patients with lung cancer. This paper provides the overview of different methods used in the retrieval system of lung nodules by a comprehensive review of existing literature. Firstly, the high level features of DICOM CT images are used for retrieval of filtered lung images from the database. The preprocessing step is used for separation of lungs fields on the filtered images. Linear Binary Pattern extracts the low level features from extracted lung areas to perform the segmentation. The technique of template matching further uses to retrieve the abnormal nodules from Lung data set.