41 research outputs found
Single Frame Image super Resolution using Learned Directionlets
In this paper, a new directionally adaptive, learning based, single image
super resolution method using multiple direction wavelet transform, called
Directionlets is presented. This method uses directionlets to effectively
capture directional features and to extract edge information along different
directions of a set of available high resolution images .This information is
used as the training set for super resolving a low resolution input image and
the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform
recovers the super-resolved high resolution image. The simulation results
showed that the proposed approach outperforms standard interpolation techniques
like Cubic spline interpolation as well as standard Wavelet-based learning,
both visually and in terms of the mean squared error (mse) values. This method
gives good result with aliased images also.Comment: 14 pages,6 figure
Template based Mole Detection for Face Recognition
Face recognition is used for personal identification. The Template based Mole Detection for Face Recognition (TBMDFR) algorithm is proposed to verify authentication of a person by detection and validation of
prominent moles present in the skin region of a face. Normalized Cross Correlation (NCC) matching, complement of Gaussian template and skin segmen
tation is used to identify and validate mole by fixing predefined NCC threshold values. It is observed that the NCC values of TBMDFR are much higher
compared to the existing algorithms
Face hallucination with application in far distance face recognition
In this thesis, faces captured in far distances are investigated. Face enhancement algorithms are studied. Hallucinating faces in holistic model and patch-based model are analysed respectively. The advantages and disadvantages of both models are discussed. An innovative holistic model and patch-based model are proposed separately. More investigation in practical surveillance environments are carried. And a new far face recognition model is proposed. Experiments demonstrate the improvement of proposed approaches
Valvekaameratel põhineva inimseire täiustamine pildi resolutsiooni parandamise ning näotuvastuse abil
Due to importance of security in the society, monitoring activities and recognizing specific
people through surveillance video camera is playing an important role. One of
the main issues in such activity rises from the fact that cameras do not meet the resolution
requirement for many face recognition algorithms. In order to solve this issue,
in this work we are proposing a new system which super resolve the image. First,
we are using sparse representation with the specific dictionary involving many natural
and facial images to super resolve images. As a second method, we are using deep
learning convulutional network. Image super resolution is followed by Hidden Markov
Model and Singular Value Decomposition based face recognition. The proposed system
has been tested on many well-known face databases such as FERET, HeadPose, and
Essex University databases as well as our recently introduced iCV Face Recognition
database (iCV-F). The experimental results shows that the recognition rate is increasing
considerably after applying the super resolution by using facial and natural image
dictionary. In addition, we are also proposing a system for analysing people movement
on surveillance video. People including faces are detected by using Histogram of Oriented
Gradient features and Viola-jones algorithm. Multi-target tracking system with
discrete-continuouos energy minimization tracking system is then used to track people.
The tracking data is then in turn used to get information about visited and passed
locations and face recognition results for tracked people
Evaluation of face recognition algorithms under noise
One of the major applications of computer vision and image processing is face recognition,
where a computerized algorithm automatically identifies a person’s face from
a large image dataset or even from a live video. This thesis addresses facial recognition,
a topic that has been widely studied due to its importance in many applications
in both civilian and military domains. The application of face recognition systems
has expanded from security purposes to social networking sites, managing fraud, and
improving user experience. Numerous algorithms have been designed to perform face
recognition with good accuracy. This problem is challenging due to the dynamic nature
of the human face and the different poses that it can take. Regardless of the
algorithm, facial recognition accuracy can be heavily affected by the presence of noise.
This thesis presents a comparison of traditional and deep learning face recognition
algorithms under the presence of noise. For this purpose, Gaussian and salt-andpepper
noises are applied to the face images drawn from the ORL Dataset. The
image recognition is performed using each of the following eight algorithms: principal
component analysis (PCA), two-dimensional PCA (2D-PCA), linear discriminant
analysis (LDA), independent component analysis (ICA), discrete cosine transform
(DCT), support vector machine (SVM), convolution neural network (CNN) and Alex
Net. The ORL dataset was used in the experiments to calculate the evaluation accuracy
for each of the investigated algorithms. Each algorithm is evaluated with two
experiments; in the first experiment only one image per person is used for training,
whereas in the second experiment, five images per person are used for training. The investigated traditional algorithms are implemented with MATLAB and the deep
learning algorithms approaches are implemented with Python. The results show that
the best performance was obtained using the DCT algorithm with 92% dominant
eigenvalues and 95.25 % accuracy, whereas for deep learning, the best performance
was using a CNN with accuracy of 97.95%, which makes it the best choice under noisy
conditions