Face Detection and Recognition in Indoor Environment

This thesis examines and implements some state-of-the-art methods for human face recognition. In order to examine all aspects of the face recognition task at a generic level, we have divided the task into three consecutive steps: 1) Skin color detection (for identifying regions with skin-colored), 2) Face detection (no identification) and 3) Face recognition (for identifying which face is detected). Using a statistical model for the color region we can narrow down the possible regions in which faces could be found. Furthermore, trying to do an identification on a region that does not include a face is inefficient, since identification is a computationally complex process. Therefore, using a faster and less complex algorithm to do the general face detection before face recognition is a faster way to identify faces. In this work, we use a machine learning approach for boosting weaker hypotheses into a stronger one in order to detect faces. For face recognition, we look deeper into the appearance of the face and define the identity as a specific texture that we try to recognize under different appearances. Finally, we merge all the different steps into an integrated system for full-frontal face detection and recognition. We evaluate the system based on accuracy and performance. Acknowledgement This research would not have been started at all without the great response in the early days from Alireza Tavakoli. Without the encouraging support from my supervisors, Alireza Tavakoli and Babak Rasolzadeh at the Royal Institute of Technology (KTH-CVAP), there would have been very little to write. To this end, the great interest shown by my examiner Axel Ruhe at the Royal Institute of Technology (KTH-NA) has been encouraging.