2 research outputs found
Facial Expression Recognition Based on Complexity Perception Classification Algorithm
Facial expression recognition (FER) has always been a challenging issue in
computer vision. The different expressions of emotion and uncontrolled
environmental factors lead to inconsistencies in the complexity of FER and
variability of between expression categories, which is often overlooked in most
facial expression recognition systems. In order to solve this problem
effectively, we presented a simple and efficient CNN model to extract facial
features, and proposed a complexity perception classification (CPC) algorithm
for FER. The CPC algorithm divided the dataset into an easy classification
sample subspace and a complex classification sample subspace by evaluating the
complexity of facial features that are suitable for classification. The
experimental results of our proposed algorithm on Fer2013 and CK-plus datasets
demonstrated the algorithm's effectiveness and superiority over other
state-of-the-art approaches
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis
Dementia is a syndrome characterised by the decline of different cognitive
abilities. Alzheimer's Disease (AD) is the most common dementia affecting
cognitive domains such as memory and learning, perceptual-motion or executive
function. High rate of deaths and high cost for detection, treatments and
patient's care count amongst its consequences. Early detection of AD is
considered of high importance for improving the quality of life of patients and
their families. The aim of this thesis is to introduce novel non-invasive early
diagnosis methods in order to speed the diagnosis, reduce the associated costs
and make them widely accessible. Novel AD's screening tests based on virtual
environments using new immersive technologies combined with advanced Human
Computer Interaction (HCI) systems are introduced. Four tests demonstrate the
wide range of screening mechanisms based on cognitive domain impairments that
can be designed using virtual environments. The use of emotion recognition to
analyse AD symptoms has been also proposed. A novel multimodal dataset was
specifically created to remark the autobiographical memory deficits of AD
patients. Data from this dataset is used to introduce novel descriptors for
Electroencephalogram (EEG) and facial images data. EEG features are based on
quaternions in order to keep the correlation information between sensors,
whereas, for facial expression recognition, a preprocessing method for motion
magnification and descriptors based on origami crease pattern algorithm are
proposed to enhance facial micro-expressions. These features have been proved
on classifiers such as SVM and Adaboost for the classification of reactions to
autobiographical stimuli such as long and short term memories.Comment: PhD Thesis 201