Representation of facial expression categories in continuous arousal–valence space: Feature and correlation

Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal–valence (AV) dimensional space. The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories.     

Effects of progesterone intravaginal devices on synchronization of estrus in postpartum dairy cows

Effects of two intravaginal treatments of progesterone on the synchronization of behavioral estrus were compared in three groups of lactating dairy cows at 7 to 8 wk postpartum. Group 1 had progesterone-releasing intravaginal devices with attached estradiol benzoate capsules inserted for 12 d. Groups 2 and 3 had controlled internal drug release devices, containing progesterone alone, inserted for 9 or 12 d. Behavioral estrus was detected as early as 24, 32, and 36 h after removal of the devices in groups 1, 2, and 3, respectively. Estrus was better synchronized and the proportion of cows showing estrus after device removal was greater in group 1 than in groups 2 and 3. After removal of devices, mean milk progesterone of all cows that did not show estrus was higher than that of cows that showed estrus. In groups 1, 2, and 3, 1 of 7, 7 of 12, and 4 of 12 cows not showing estrus had high milk progesterone after removal of the devices, indicating the presence of functional corpora lutea. In cows not displaying estrus milk progesterone concentrations decreased, indicating these cows were cyclic, although behavioral estrus was absent.     

Highlights for more complete sports video summarization

Summarization is an essential requirement for achieving a more compact and interesting representation of sports video contents. We propose a framework that integrates highlights into play segments and reveal why we should still retain breaks. Experimental results show that fast detections of whistle sounds, crowd excitement, and text boxes can complement existing techniques for play-breaks and highlights localization.     

Facial expression recognition experiments with data from television broadcasts and the World Wide Web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.