Face detection : A comparison between histogram thresholding and neural networks - Jamal Ahmad Dargham (2008)
Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this thesis, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. Multi-layer perceptron (MLP) neural networks and histogram thresholding techniques have been used for skin and lips detection. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web. Only the skin and the lips colour in the normalised RGB colour scheme were used for the skin and lips detection respectively. A new method for obtaining the r, g, and b components of the normalised RGB systems from the R, G, and B components of the RGB system was proposed. It was found out that the proposed method, called maximum intensity normalisation, gives higher percentage of correct skin detection than the conventional rgb colour scheme regardless of the database used or the skin detection method. Two methods were used to find the number of neurons in the hidden layer of the MLP. The first method use binary search between a minimum and a maximum values while the second method use sequential search with a stopping criteria. The effect of scale factor, facial expressions and minor occlusions with glasses on skin, lips and face detection was investigated. It was found out that, as the scale factor increases the percentage skin and lips detection error decreases. However, the percentage decrease in skin and lips detection errors depends on the intensity normalisation, the detection method and the chrominance component used. But the scale factor did not have any effect on the face detection. In general, the facial expression did not have any significant effect on skin detection. However, for lips detection, the laughing expression did give the highest lips detection error followed by smiling expression. Furthermore, the percentage increase in lips detection error as a result of the facial expression depends on the intensity normalisation, the detection method and the chrominance component used. As for face detection, the facial expression has a negative effect on the correct face detection especially at scale factor of 3. Although, the minor occlusion increases the skin detection error it has no significant effect on the performance of face detection.