You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Detecting Faces in Images : A Survey 
Chapter Title:  Texture 
1. Abstract of Cited Paper (BIB001):  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.

Literature Review:
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.