12,658 research outputs found
PATH: Person Authentication using Trace Histories
In this paper, a solution to the problem of Active Authentication using trace
histories is addressed. Specifically, the task is to perform user verification
on mobile devices using historical location traces of the user as a function of
time. Considering the movement of a human as a Markovian motion, a modified
Hidden Markov Model (HMM)-based solution is proposed. The proposed method,
namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities
of location and timing information of the observations to smooth-out the
emission probabilities while training. Hence, it can efficiently handle
unforeseen observations during the test phase. The verification performance of
this method is compared to a sequence matching (SM) method , a Markov
Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active
Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The
proposed MSHMM method outperforms the compared methods in terms of equal error
rate (EER). Additionally, the effects of different parameters on the proposed
method are discussed.Comment: 8 pages, 9 figures. Best Paper award at IEEE UEMCON 201
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Porting concepts from DNNs back to GMMs
Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the 'deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination
LOMo: Latent Ordinal Model for Facial Analysis in Videos
We study the problem of facial analysis in videos. We propose a novel weakly
supervised learning method that models the video event (expression, pain etc.)
as a sequence of automatically mined, discriminative sub-events (eg. onset and
offset phase for smile, brow lower and cheek raise for pain). The proposed
model is inspired by the recent works on Multiple Instance Learning and latent
SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in
the videos, approximately. We obtain consistent improvements over relevant
competitive baselines on four challenging and publicly available video based
facial analysis datasets for prediction of expression, clinical pain and intent
in dyadic conversations. In combination with complimentary features, we report
state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR
Detection of major ASL sign types in continuous signing for ASL recognition
In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker
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