Face detection from cluttered images using a polynomial neural network
Automatic detection of human faces from cluttered images is important for face recognition and security applications. This problem is challenging due to the multitude of variations and the confusion between face and background regions. This paper proposes a new face detection method using a polynomial neural network (PNN). To locate the human faces in an image, the local regions in multiscale sliding windows are classified by the PNN to two classes, namely, face and non-face. The PNN takes as inputs the binomials of the projection of the local image onto a feature subspace learned by principal component analysis (PCA). We investigated the influence of PCA on either the face samples or the pooled face and non-face samples. In addition, we integrate the distance from the feature subspace into the PNN to improve the detection performance. In experiments on images with complex backgrounds, the proposed method has produced promising results in terms of high detection rate and low false positive rate.
face detection polynomial neural network face recognition pattern classification feature extraction