Face detection from cluttered images using a polynomial neural network

In this paper, we propose a new method for face detection from cluttered images. We use a polynomial neural network (PNN) for separation of face and non-face patterns while the complexity of the PNN is reduced by principal component analysis (PCA). In face detection, the PNN is used to classify sliding windows in multiple scales and label the windows that contain a face. The PNN is shown to be powerful to discriminate between face and non-face patterns when trained with large number of samples. In experiments on images with simple or complex backgrounds, the proposed method has achieved high detection rate and low false positive rate.

• Type: Conference
• Year: 2001
• Where: ICIP
• Authors: Lin-Lin Huang, Akinobu Shimizu, Yoshihiro Hagihara, Hidefumi Kobatake
• Keywords: Face Detection, False Positive Rate, ICIP 2001, Image Processing, Polynomial Neural Network