Face Detection in Skin-Toned Images Through Wavelet Edges and Neural Network

Abstract

Abstract-In this paper, an improved algorithm to detect faces in images with skin tone regions is proposed. Among the segmented candidate regions, facial edges detected using Canny and Sobel operators on wavelet approximation are suggested as feature set. These features are further classified using a neural-net proposed. Results of the test using the proposed algorithm are compared with those of the previous ones.

Feature-Based Face Detection: A Survey

“…Another proposed system [19]applied some types of noise removable then he formed a skin map, and search in each detected skin color region on two eye blobs, if eyes are founded then it is a face else it is regarded as a non-face, this method showed 2308 successful face detection from 2615 tested images which mean 88.26% true detection rate. Just like skin color, skin also has a texture feature that can be exploit to isolate face from background, In [20], a novel detection algorithim is proposed uses combination of edge and sckin color features this increase the effeciency of detecting faces and leading to decrease false acceptnece rate, this algorithm gain 21 false acceptness while it was 128 in case of using skin texture feature only.…”