19,444 research outputs found
Learning to recognise talking faces
An approach for person identification is described based on spatio-temporal analysis of the talking face. A person is represented by a parametric model of the visible speech articulators and their temporal characteristics during speech production. The model consists of shape parameters, representing the lip contour and intensity parameters representing the grey level distribution in the mouth region. The model is used to track lips in image sequences where the model parameters are recovered from the tracking results. While some of these parameters relate to speech information, others are intuitively related to different persons and we show that models based on these features enable successful person identification. We model the shape and intensity parameters as mixtures of Gaussians and their temporal dependencies by Hidden Markov Models. Identifying a talking person is performed by estimating the likelihood of each model for having generated the observed sequence of features and the model with the highest likelihood is chosen as the identified person
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Audio-Visual Speaker Identification using the CUAVE Database
The freely available nature of the CUAVE database allows it to provide a valuable platform to form benchmarks and compare research. This paper shows that the CUAVE database can successfully be used to test speaker identifications systems, with performance comparable to existing systems implemented on other databases. Additionally, this research shows that the optimal configuration for decisionfusion of an audio-visual speaker identification system relies heavily on the video modality in all but clean speech conditions
You said that?
We present a method for generating a video of a talking face. The method
takes as inputs: (i) still images of the target face, and (ii) an audio speech
segment; and outputs a video of the target face lip synched with the audio. The
method runs in real time and is applicable to faces and audio not seen at
training time.
To achieve this we propose an encoder-decoder CNN model that uses a joint
embedding of the face and audio to generate synthesised talking face video
frames. The model is trained on tens of hours of unlabelled videos.
We also show results of re-dubbing videos using speech from a different
person.Comment: https://youtu.be/LeufDSb15Kc British Machine Vision Conference
(BMVC), 201
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
Disguised face identification (DFI) is an extremely challenging problem due
to the numerous variations that can be introduced using different disguises.
This paper introduces a deep learning framework to first detect 14 facial
key-points which are then utilized to perform disguised face identification.
Since the training of deep learning architectures relies on large annotated
datasets, two annotated facial key-points datasets are introduced. The
effectiveness of the facial keypoint detection framework is presented for each
keypoint. The superiority of the key-point detection framework is also
demonstrated by a comparison with other deep networks. The effectiveness of
classification performance is also demonstrated by comparison with the
state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision
Workshops (ICCVW) 201
Taking the bite out of automated naming of characters in TV video
We investigate the problem of automatically labelling appearances of characters in TV or film material
with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying
when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”
Language Identification Using Visual Features
Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
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