|dc.description.abstract||Facial expression recognition (FER) is a powerful tool that is emerging rapidly due to increased
computational power in current technologies. It has many applications in the fields of human-computer
interaction, psychological behaviour analysis, and image understanding. However,
FER presently is not fully realised due to the lack of an effective facial feature descriptor.
The covariance matrix as a feature descriptor is popular in object detection and texture recognition.
Its innate ability to fuse multiple local features within a domain is proving to be useful in
applications such as biometrics. Developing methods also prevalent in pattern recognition are
local texture patterns such as Local Binary Pattern (LBP) and Local Directional Pattern (LDP)
because of their fast computation and robustness against illumination variations. This study will
examine the performance of covariance feature descriptors that incorporate local texture patterns
concerning applications in facial expression recognition. The proposed method will focus on
generating feature descriptors to extract robust and discriminative features that can aid against
extrinsic factors affecting facial expression recognition, such as illumination, pose, scale, rotation
and occlusion. The study also explores the influence of using holistic versus componentbased
approaches to FER.
A novel feature descriptor referred to as Local Directional Covariance Matrices (LDCM) is proposed.
The covariance descriptors will consist of fusing features such as location, intensity and
filter responses, and include LBP and LDP into the covariance structure. Tests conducted will
examine the accuracy of different variations of covariance features and the impact of segmenting
the face into equal sized blocks or special landmark regions, i.e. eyes, nose and mouth, for
classification. The results on JAFFE, CK+ and ISED facial expression databases establish that
the proposed descriptor achieves a high level of performance for FER at a reduced feature size.
The effectiveness of using a component-based approach with special landmarks displayed stable
results across different datasets and environments.||en_US