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Anjan Dutta; Umapada Pal; Josep Llados |
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Title |
Compact Correlated Features for Writer Independent Signature Verification |
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2016 |
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23rd International Conference on Pattern Recognition |
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This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. |
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Cancun; Mexico; December 2016 |
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DAG; 600.097 |
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Admin @ si @ DPL2016 |
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2875 |
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Hugo Jair Escalante; Victor Ponce; Jun Wan; Michael A. Riegler; Baiyu Chen; Albert Clapes; Sergio Escalera; Isabelle Guyon; Xavier Baro; Pal Halvorsen; Henning Muller; Martha Larson |
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Title |
ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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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 was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks. |
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Cancun; Mexico; December 2016 |
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HuPBA; 602.143;MV |
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Admin @ si @ EPW2016 |
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2827 |
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Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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With whom do I interact with? Social interaction detection in egocentric photo-streams |
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2016 |
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23rd International Conference on Pattern Recognition |
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Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. |
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Cancun; Mexico; December 2016 |
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MILAB |
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Admin @ si @ADR2016a |
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2791 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams |
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Conference Article |
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2016 |
Publication |
23rd International Conference on Pattern Recognition |
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Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. |
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Cancun; Mexico; December 2016 |
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MILAB |
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Admin @ si @ ADR2016d |
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2835 |
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Author |
Marc Bolaños; Petia Radeva |
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Title |
Simultaneous Food Localization and Recognition |
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Conference Article |
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Year |
2016 |
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23rd International Conference on Pattern Recognition |
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CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
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Cancun; Mexico; December 2016 |
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MILAB; no proj |
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Admin @ si @ BoR2016 |
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2834 |
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Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari |
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Title |
Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images |
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Conference Article |
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Year |
2016 |
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23rd International Conference on Pattern Recognition |
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10165 |
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Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution. |
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Cancun; Mexico; December 2016 |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ BHR2016 |
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2902 |
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