A Master of Science thesis in Electrical Engineering by Kamal Adel Abuqaaud entitled, "Face Recognition in Uncontrolled Indoor Environment," submitted in June 2013. Thesis advisor is Dr. Khaled Assaleh and Co-advisor is Dr. Tamer Shanableh. Available are both soft and hard copies of the thesis.
Face recognition (FR) is one of the most convenient biometric systems even though it is not currently the most reliable one. Especially when images for (FR) system are captured by surveillance cameras, such cameras often produce low quality images which make recognition more difficult and less reliable. This study uses a recently published database called "SCface database" which emphasizes the challenges of face recognition in uncontrolled indoor conditions such as lighting conditions, face pose, facial expression and distance to camera. More specifically, the recognition is done using different cameras of different resolutions and imaging sensors. The aim of this study is to examine the effect of camera quality and distance from the camera with regards to face recognition rates by analyzing different face recognition schemes such as Eigenfaces, Discrete Cosine Transform (DCT), Wavelet Transform, Gray Level Concurrence Matrix (GLCM) and Spatial Differential Operators (SDO). Principal Component Analysis (PCA), Zonal coding and spectral regression were also investigated as various dimensionality reduction approaches. At the classification stage a variety types of classifiers were tested and compared such as: Linear Discriminant Function (LDF), KNN classifier, polynomial classifiers and Neural Networks. As a result we developed a reliable face recognition system that recognizes faces captured by different cameras in terms of quality and resolution at different distances in surveillance conditions. In our proposed algorithm, face images are preprocessed by means of; skin segmentation, color transformation, cropping, normalization and filtering. Then both Spatial Differential Operators (SDO) and Discrete Cosine Transform (DCT) are applied to extract features, and Principal Component Analysis (PCA) is employed to reduce dimensionality. Linear Discriminant Function (LDF) is utilized as a classifier. The proposed system is compared with the well-known eigenfaces recognition solution. Experimental results show that the proposed system yields superior recognition rates compared to those obtained by the recently published solutions.