96,440 research outputs found
Face Detection And Lip Localization
Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery.
We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions.
Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
End-to-end Projector Photometric Compensation
Projector photometric compensation aims to modify a projector input image
such that it can compensate for disturbance from the appearance of projection
surface. In this paper, for the first time, we formulate the compensation
problem as an end-to-end learning problem and propose a convolutional neural
network, named CompenNet, to implicitly learn the complex compensation
function. CompenNet consists of a UNet-like backbone network and an autoencoder
subnet. Such architecture encourages rich multi-level interactions between the
camera-captured projection surface image and the input image, and thus captures
both photometric and environment information of the projection surface. In
addition, the visual details and interaction information are carried to deeper
layers along the multi-level skip convolution layers. The architecture is of
particular importance for the projector compensation task, for which only a
small training dataset is allowed in practice. Another contribution we make is
a novel evaluation benchmark, which is independent of system setup and thus
quantitatively verifiable. Such benchmark is not previously available, to our
best knowledge, due to the fact that conventional evaluation requests the
hardware system to actually project the final results. Our key idea, motivated
from our end-to-end problem formulation, is to use a reasonable surrogate to
avoid such projection process so as to be setup-independent. Our method is
evaluated carefully on the benchmark, and the results show that our end-to-end
learning solution outperforms state-of-the-arts both qualitatively and
quantitatively by a significant margin.Comment: To appear in the 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Source code and dataset are available at
https://github.com/BingyaoHuang/compenne
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
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