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Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original...
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images...
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based...
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a...
Human gesture recognition is one of the central research fields of computer vision, and effective gesture recognition is still challenging up to now. In this paper, we present a pyramidal 3D convolutional network framework for large-scale isolated human gesture recognition. 3D convolutional networks are utilized to learn the spatiotemporal features from gesture video files. Pyramid input is proposed...
The gesture recognition has raised attention in computer vision owing to its many applications. However, video-based large-scale gesture recognition still faces many challenges, since many factors like background may disturb the accuracy. To achieve gesture recognition with large-scale videos, we propose a method based on RGB-D data. To learn gesture details better, the inputs are expanded into 32-frame...
In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based...
In this paper, we focus on describing the method we designed for automatic perceived personality prediction. We present a simple model that uses three different sets of features: nonverbal audio cues, visual cues from video, and facial landmark points. The model uses a random decision forest to do regression from the extracted features. As we discuss in Section 4, this multimodal model performs relatively...
Affective computing, particularly emotion and personality trait recognition, is of increasing interest in many research disciplines. The interplay of emotion and personality shows itself in the first impression left on other people. Moreover, the ambient information, e.g. the environment and objects surrounding the subject, also affect these impressions. In this work, we employ pre-trained Deep Convolutional...
In this paper, we propose using 3D Convolutional Neural Networks for large scale user-independent continuous gesture recognition. We have trained an end-to-end deep network for continuous gesture recognition (jointly learning both the feature representation and the classifier). The network performs three-dimensional (i.e. space-time) convolutions to extract features related to both the appearance...
The task of the ChaLearn Apparent Personality Analysis: First Impressions Challenge is to rate/quantify personality traits of users in short video sequences. Although the validity of personality judgments from short interactions is questionable, studies show the possibility of predicting attributed traits (First Impressions) using facial [15] and acoustic [13] features. The challenge introduces a...
In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set...
This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track...
This contest is a joint initiative organized by the University of Salerno (Italy) and the University of Queensland (Australia) with the support of the Sullivan Nicolaides Pathology (SNP), Australia. The contest primarily aims to provide a platform for scientists and practitioners for performing research to develop Computer Aided Diagnosis (CAD) systems for pathology tests utilizing indirect immunofluorescence...
As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification...
A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope (IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations...
Human Epithelial type-2 (HEp-2) cells are used as substrates for the detection of Anti Nuclear Antibodies (ANA) in the Indirect Immunofluorescence (IIF) test to diagnose autoimmune diseases. Pathologists in the laboratory examine the IIF slides to detect and recognize theHEp-2 cell patterns to generate the report. So, the IIF test is subjective and requires objective analysis. This paper introduces...
Human epithelial (HEp-2) cell specimens are obtained from indirect immunofluorescence (IIF) imaging for diagnosis and management of autoimmune diseases. Analysis of HEp2 cells is important and in this work we consider automatic cell segmentation and classification using spatial and texture pattern features and random forest classifiers. In this paper, we summarize our efforts in classification and...
Reliable automatic system for Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of systemic autoimmune diseases. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to address the HEp-2 specimen classification problem. The FCN in the proposed framework was adapted from VGG-16, which was trained with ICPR 2016 dataset...
Despite being an essential prerequisite at the basis of many applications ranging from surveillance to computational photography, the problem of initial background estimation seems to be marginally investigated. In this paper, we present a reliable CNN-based solution to estimate the initial background (BG) of a scene, given not necessarily a whole sequence but just a small set of frames containing...
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