Detecting Faces in Images : A Survey <s> Texture <s> Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network. <s> BIB001 </s> Detecting Faces in Images : A Survey <s> Texture <s> This paper proposed a new method for the full face detection from complex backgrounds. Based on feature parameters of space gray-level dependence (SGLD) matrix, the face-texture model composed by a set of inequalities was derived. A color information utilization incorporated with the face-texture feature was also investigated. Using the face-texture model, we designed a kind of scanning scheme for face detection in color scenes, in which the orange-like parts including the face areas were enhanced by utilizing the I component of the YIQ color system. The experiments showed that this method can locate the face position in the complex backgrounds effectively. <s> BIB002
Human faces have a distinct texture that can be used to separate them from different objects. Augusteijn and Skufca developed a method that infers the presence of a face through the identification of face-like textures  . The texture are computed using second-order statistical features (SGLD)  on subimages of 16 Â 16 pixels. Three types of features are considered: skin, hair, and others. They used a cascade correlation neural network BIB001 for supervised classification of textures and a Kohonen self-organizing feature map  to form clusters for different texture classes. To infer the presence of a face from the texture labels, they suggest using votes of the occurrence of hair and skin textures. However, only the result of texture classification is reported, not face localization or detection. Dai and Nakano also applied SGLD model to face detection BIB002 . Color information is also incorporated with the face-texture model. Using the face texture model, they design a scanning scheme for face detection in color scenes in which the orange-like parts including the face areas are enhanced. One advantage of this approach is that it can detect faces which are not upright or have features such as beards and glasses. The reported detection rate is perfect for a test set of 30 images with 60 faces.