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
