Multiple kernel learning SVM and statistical validation for facial landmark detection

Abstract — In this paper we present a robust and accurate method to detect 17 facial landmarks in expressive face images. We introduce a new multi-resolution framework based on the recent multiple kernel algorithm. Low resolution patches carry the global information of the face and give a coarse but robust detection of the desired landmark. High resolution patches, using local details, refine this location. This process is combined with a bootstrap process and a statistical validation, both improving the system robustness. Combining independent point detection and prior knowledge on the point distribution, the proposed detector is robust to variable lighting conditions and facial expressions. This detector is tested on several databases and the results reported can be compared favorably with the current state of the art point detectors. I

Robust continuous prediction of human emotions using multiscale dynamic cues

Designing systems able to interact with humans in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the Audio/Visual Emotion Challenge (AVEC’12) whose goal is to continuously predict four affective signals describing human emotions (namely valence, arousal, expectancy and power). The proposed method uses log-magnitude Fourier spectra to extract multiscale dynamic descriptions of signals characterizing global and local face appearance as well as head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. For selecting features, we introduce a new correlation-based measure that takes into account a possible delay between the labels and the data and significantly increases robustness. We also propose a particularly fast regressor-level fusion framework to merge systems based on di↵erent modalities. Experiments have proven the e ciency of each key point of the proposed method and we obtain very promising results

Combinaison de Descripteurs Hétérogènes pour la Reconnaissance de Micro-Mouvements Faciaux

Session "Posters"National audienceDans cet article, nous présentons notre réponse au premier challenge international sur la reconnaissance et l'analyse d'émotions faciales (Facial Emotion Recognition and Analysis Challenge). Nous proposons une combinaison de dif- férents types de descripteurs dans le but de détecter de manière automatique, les micro-mouvements faciaux d'un visage. Ce système utilise une Machine à Vecteurs Supports Multi-Noyaux pour chacune des Action Units (AU) que nous désirons détecter. Le premier noyau est calculé en utilisant des histogrammes de motifs binaires locaux de Gabor (ou Local Gabor Binary Pattern, LGBP) via un noyau d'intersection d'histogramme. Le second noyau quant à lui, est crée avec des coefficients de Modèles Actifs d'Apparence via un noyau gaussien. Les sorties de chacune des SVM sont ensuite filtrées dans le but d'inclure l'informa- tion temporelle de la séquence. Afin d'évaluer notre système, nous avons procédé à de nombreuses expérimentations sur plusieurs points clefs de notre méthode. Enfin, nous comparons nos résultats à ceux obtenus par les autres participants au challenge, tout en analysant nos performanche

Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

International audienceThis paper presents our response to the first interna- tional challenge on Facial Emotion Recognition and Analysis. We propose to combine different types of features to automatically detect Action Units in facial images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and an RBF kernel. During the training step, we combine these two types of features using the recently proposed SimpleMKL algorithm. SVM outputs are then averaged to exploit temporal information in the sequence. To eval- uate our system, we perform deep experimentations on several key issues: influence of features and kernel function in histogram- based SVM approaches, influence of spatially-independent in- formation versus geometric local appearance information and benefits of combining both, sensitivity to training data and interest of temporal context adaptation. We also compare our results to those of the other participants and try to explain why our method had the best performance during the FERA challenge

Conserved enhancers control notochord expression of vertebrate Brachyury.

The cell type-specific expression of key transcription factors is central to development and disease. Brachyury/T/TBXT is a major transcription factor for gastrulation, tailbud patterning, and notochord formation; however, how its expression is controlled in the mammalian notochord has remained elusive. Here, we identify the complement of notochord-specific enhancers in the mammalian Brachyury/T/TBXT gene. Using transgenic assays in zebrafish, axolotl, and mouse, we discover three conserved Brachyury-controlling notochord enhancers, T3, C, and I, in human, mouse, and marsupial genomes. Acting as Brachyury-responsive, auto-regulatory shadow enhancers, in cis deletion of all three enhancers in mouse abolishes Brachyury/T/Tbxt expression selectively in the notochord, causing specific trunk and neural tube defects without gastrulation or tailbud defects. The three Brachyury-driving notochord enhancers are conserved beyond mammals in the brachyury/tbxtb loci of fishes, dating their origin to the last common ancestor of jawed vertebrates. Our data define the vertebrate enhancers for Brachyury/T/TBXTB notochord expression through an auto-regulatory mechanism that conveys robustness and adaptability as ancient basis for axis development

Loss-of-function variants in CUL3 cause a syndromic neurodevelopmental disorder

Purpose De novovariants inCUL3(Cullin-3 ubiquitin ligase) have been strongly associated with neurodevelopmental disorders (NDDs), but no large case series have been reported so far. Here we aimed to collect sporadic cases carrying rare variants inCUL3,describe the genotype-phenotype correlation, and investigate the underlying pathogenic mechanism.MethodsGenetic data and detailed clinical records were collected via multi-center collaboration. Dysmorphic facial features were analyzed using GestaltMatcher. Variant effects on CUL3 protein stability were assessed using patient-derived T-cells.ResultsWe assembled a cohort of 35 individuals with heterozygousCUL3variants presenting a syndromic NDD characterized by intellectual disability with or without autistic features. Of these, 33 have loss-of-function (LoF) and two have missense variants.CUL3LoF variants in patients may affect protein stability leading to perturbations in protein homeostasis, as evidenced by decreased ubiquitin-protein conjugatesin vitro. Specifically, we show that cyclin E1 (CCNE1) and 4E-BP1 (EIF4EBP1), two prominent substrates of CUL3, fail to be targeted for proteasomal degradation in patient-derived cells.ConclusionOur study further refines the clinical and mutational spectrum ofCUL3-associated NDDs, expands the spectrum of cullin RING E3 ligase-associated neuropsychiatric disorders, and suggests haploinsufficiency via LoF variants is the predominant pathogenic mechanism

Playful activity post-learning improves training performance in Labrador Retriever dogs (Canis lupus familiaris)

Situations that are emotional and arousing have an effect on cognitive performance. It is thought that beta adrenergic activation and the release of stress hormones enhance memory consolidation and lead to an increase in memorability of emotional events. This beneficial effect has been shown in humans, non-human primates and rodents. Techniqueswhich could enhancememory for learning specific taskswould be highly valuable, especially in dogs, which are extensively trained to aid humans. A pseudo-randomized, counterbalanced, between subject study designs was utilised and 16 Labrador Retrievers ranging from 1 to 9 years of agewere trained in a 2-choice discrimination paradigm. After task acquisition, either a playful activity intervention (N= 8) or a resting period (N= 8) took place, lasting for 30 min. A range of factors including age, sex, training experience and trials to criterion on each day was subjected to a multiple factor/covariate General Linear Model analysis. The results show that playful activity post-learning improved training performance evidenced by fewer trials needed to re-learn the task 24 h after initial acquisition (playful activity group: mean number of trials 26, SD 6; resting group: mean number of trials 43, SD 19, effect size 1.2). Average heart rate, as a measure of arousal, during the interventionwas significantly higher in the playful activity group (143 beats/min, SD 16) versus the resting group (86 beats/min, SD 19, P b 0.001). Salivary cortisol did not significantly differ between groups during training, however a significant decrease (T:−4.1 P b 0.01) was seen after the playful activity. To our knowledge this is the first evidence that posttraining activity may influence training performance in dogs

Imaging practice in low-grade gliomas among European specialized centers and proposal for a minimum core of imaging

Objective: Imaging studies in diffuse low-grade gliomas (DLGG) vary across centers. In order to establish a minimal core of imaging necessary for further investigations and clinical trials in the field of DLGG, we aimed to establish the status quo within specialized European centers. Methods: An online survey composed of 46 items was sent out to members of the European Low-Grade Glioma Network, the European Association of Neurosurgical Societies, the German Society of Neurosurgery and the Austrian Society of Neurosurgery. Results: A total of 128 fully completed surveys were received and analyzed. Most centers (n=96, 75%) were academic and half of the centers (n=64, 50%) adhered to a dedicated treatment program for DLGG. There were national differences regarding the sequences enclosed in MRI imaging and use of PET, however most included T1 (without and with contrast, 100%), T2 (100%) and TIRM or FLAIR (20, 98%). DWI is performed by 80% of centers and 61% of centers regularly performed PWI.ConclusionA minimal core of imaging composed of T1 (w/wo contrast), T2, TIRM/FLAIR, PWI and DWI could be identified. All morphologic images should be obtained in a slice thickness of 3mm. No common standard could be obtained regarding advanced MRI protocols and PET. Importance of the study: We believe that our study makes a significant contribution to the literature because we were able to determine similarities in numerous aspects of LGG imaging. Using the proposed minimal core of imaging in clinical routine will facilitate future cooperative studies