1,721,032 research outputs found
Improving Pain Recognition Through Better Utilisation of Temporal Information
No full textAutomatically recognizing pain from video is a very useful application\ud
as it has the potential to alert carers to patients that are\ud
in discomfort who would otherwise not be able to communicate\ud
such emotion (i.e young children, patients in postoperative\ud
care etc.). In previous work [1], a “pain-no pain” system was\ud
developed which used an AAM-SVM approach to good effect.\ud
However, as with any task involving a large amount of video\ud
data, there are memory constraints that need to be adhered to\ud
and in the previous work this was compressing the temporal\ud
signal using K-means clustering in the training phase. In visual\ud
speech recognition, it is well known that the dynamics of the\ud
signal play a vital role in recognition. As pain recognition is\ud
very similar to the task of visual speech recognition (i.e. recognising\ud
visual facial actions), it is our belief that compressing\ud
the temporal signal reduces the likelihood of accurately recognising\ud
pain. In this paper, we show that by compressing the\ud
spatial signal instead of the temporal signal, we achieve better\ud
pain recognition. Our results show the importance of the temporal\ud
signal in recognizing pain, however, we do highlight some\ud
problems associated with doing this due to the randomness of a\ud
patient's facial actions
An investigation of HMM classifier combination ctrategies for improved audio-visual speech recognition
No full textPatch-Based Analysis of Visual Speech From Multiple Views
No full textObtaining a robust feature representation of visual speech is\ud
of crucial importance in the design of audio-visual automatic\ud
speech recognition systems. In the literature, when visual\ud
appearance based features are employed for this purpose,\ud
they are typically extracted using a "holistic" approach.\ud
Namely, a transformation of the pixel values of the entire\ud
region-of-interest (ROI) is obtained, with the ROI covering\ud
the speaker's mouth and often surrounding facial area. In\ud
this paper, we instead consider a "patch" based visual feature\ud
extraction approach, within the appearance based framework.\ud
In particular, we conduct a novel analysis to determine which\ud
areas (patches) of the mouth ROI are the most informative for visual speech. Furthermore, we extend this analysis beyond\ud
the traditional frontal views, by investigating profile views\ud
as well. Not surprisingly, and for both frontal and profile\ud
views, we conclude that the central mouth patches are the\ud
most informative, but less so than the holistic features of the\ud
entire ROI. Nevertheless, fusion of holistic and the best patch\ud
based features further improves visual speech recognition\ud
performance, compared to either feature set alone. Finally,\ud
we discuss scenarios where the patch based approach may be\ud
preferable to holistic features
Adaptive mouth segmentation using chromatic features
No full textThe automatic segmentation of the mouth from its facial background is a very difficult computer vision problem due to the low grayscale distinction between classes. Recently chromatic based segmentation has enjoyed some popularity for the purposes of mouth tracking due to its ability to distinguish between the two classes. Such systems have to be highly adaptive due to problems with colour constancy. In this paper a technique for adaptive segmentation is investigated using an unsupervised clustering technique incorporating the expectation maximisation (EM) algorithm across a variety of chromatic features. Results are presented from the M2VTS database across a number of subjects
Improved speech recognition using adaptive audio-visual fusion via a stochastic secondary classifier
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
