| Abstract [eng] |
This master work presents a research upon the problem of automatic frontal face detection within digital images. A comprehensive theoretical and practical analysis of most widely used also implementation related methods is provided. Practically implemented face detection system that is based on the following algorithms: 1) DAB (Discrete AdaBoost) cascade chosen as the most effective method with reference to scientific literature analysis results; 2) proposed symmetric exponential blurring filter; 3) proposed blurred image gradient directions naïve Bayesian classifier. The latter two steps have been composed to improve face localization precision. Implementation reliability was evaluated on publicly available face detection (BioID, MIT/CMU) and face recognition (FERET, FRGC) databases using explicitly declared detected face accepting / rejecting criteria. A comparative study of the proposed approach has been accomplished. Recommendations for further accuracy and / or speed improving are provided as well. |
