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:  Predefined Templates 
1. Abstract of Cited Paper (BIB001):  A computational approach to locating human faces in newspaper photographs is described. While the computational recognition of a well-framed face as one of a known set of faces has received some attention, the computational location of faces in varying contexts is relatively unexplored. Candidates for the locations of faces are hypothesized by extracting features in the edge-image of the photograph and matching with a model of a face profile. A face is defined by a parametric representation of each of the component parts. Knowledge contained in the caption is represented using semantic networks and is used to reason about the locations of faces of individuals in the photograph. > 
2. Abstract of Cited Paper (BIB002):  The authors adopted a model-based approach, where the shape of the object is defined in terms of several mini-templates. The mini-templates are abstract descriptions of simple geometric features like arcs and corners. Relationships between mini-templates are not rigid. Rather, they are represented by springs that allow deformation of a template in terms of its size and orientation. Cost functionals are determined empirically. The authors expect their system to generate candidate regions in a given photograph associated with a rank of its goodness. > 
3. Abstract of Cited Paper (BIB003):  We describe a computer program which understands a greyscale image of a face well enough to locate individual face features such as eyes and mouth. The program has two distinct components: modules designed to locate particular face features, usually in a restricted area; and the overall control strategy which activates modules on the basis of the current solution state, and assesses and integrates the results of each module. 
4. Abstract of Cited Paper (BIB004):  This paper presents a new method for detecting a human face, and estimating its pose while tracking it in real image sequences. The virtue of the method is that parameterized qualitative features derived from a lot of sampled facial images are introduced in the detection process, and in the face tracking process, some temporary model images of the face with various poses are synthesized by a texture mapping technique and utilized. While tracking the detected face, many model images are accumulated and the pose of the human face is estimated as a linear combination of correlations between the models. 
5. Abstract of Cited Paper (BIB005):  In this paper we address the task of segmentation of objects from photographs. A method of extraction of features based on the zero-crossings of a wavelet transform is described. The wavelet transform basis functions are derived from the second derivative of a Gaussian function. The extracted features are then used in a multilevel hypothesis generate and test algorithm to locate the objects of interest. The matching algorithm is based on the springs and templates framework of Fischler and Eschlanger (1973). The zero-crossings of the wavelet coefficients at different scales are combined in the model-matching stage to generate possible candidates. We apply this method to segment human faces from newspaper photographs. 
6. Abstract of Cited Paper (BIB006):  Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast. 
7. Abstract of Cited Paper (BIB007):  This paper presents part of an on-going project to integrate perception, attention, drives, emotions, behavior arbitration, and expressive acts for a robot designed to interact socially with humans. We present the design of a visual attention system based on a model of human visual search behavior from Wolfe (1994). The attention system integrates perceptions (motion detection, color saliency, and face popouts) with habituation effects and influences from the robot's motivational and behavioral state to create a context-dependent attention activation map. This activation map is used to direct eye movements and to satiate the drives of the motivational system. 
8. Abstract of Cited Paper (BIB008):  Abstract This paper presents a novel faster search scheme of gravity-center template matching compared with the traditional search method in an image for human face detection, which significantly saves the time consumed in rough detection of human faces in a mosaic image. Besides, the system is able to detect rather slanted faces (−25°∼25°) and faces with much horizontal rotating angles (−45°∼45°) and vertical rotating angles (−30°∼30°), which are in various sizes and at unknown locations in an unconstrained background. A specially defined recognition rate of 86.7% has been reached for such faces. 
9. Abstract of Cited Paper (BIB009):  The human face is an object that is easily located in complex scenes by infants and adults alike. Yet the development of an automated system to perform this task is extremely challenging. An attempt to solve this problem raises two important issues in object location. First, natural objects such as human faces tend to have boundaries which are not exactly described by analytical functions. Second, the object of interest (face) could occur in a scene in various sizes, thus requiring the use of scale independent techniques which can detect instances of the object at all scales.Although, the task of identifying a well-framed face (as one of a set of labeled faces) has been well researched, the task of locating a face in a natural scene is relatively unexplored. We present a computational theory for locating human faces in scenes with certain constraints. The theory will be validated by experiments confined to instances where people's faces are the primary subject of the scene, occlusion is minimal, and the faces contrast well against the background.

Literature Review:
An early attempt to detect frontal faces in photographs is reported by Sakai et al. BIB001 . They used several subtemplates for the eyes, nose, mouth, and face contour to model a face. Each subtemplate is defined in terms of line segments. Lines in the input image are extracted based on greatest gradient change and then matched against the subtemplates. The correlations between subimages and contour templates are computed first to detect candidate locations of faces. Then, matching with the other subtemplates is performed at the candidate positions. In other words, the first phase determines focus of attention or region of interest and the second phase examines the details to determine the existence of a face. The idea of focus of attention and subtemplates has been adopted by later works on face detection. Craw et al. presented a localization method based on a shape template of a frontal-view face (i.e., the outline shape of a face)  . A Sobel filter is first used to extract edges. These edges are grouped together to search for the template of a face based on several constraints. After the head contour has been located, the same process is repeated at different scales to locate features such as eyes, eyebrows, and lips. Later, Craw et al. describe a localization method using a set of 40 templates to search for facial features and a control strategy to guide and assess the results from the template-based feature detectors BIB004 . Govindaraju et al. presented a two stage face detection method in which face hypotheses are generated and tested BIB002 , BIB003 , BIB010 . A face model is built in terms of features defined by the edges. These features describe the curves of the left side, the hair-line, and the right side of a frontal face. The Marr-Hildreth edge operator is used to obtain an edge map of an input image. A filter is then used to remove objects whose contours are unlikely to be part of a face. Pairs of fragmented contours are linked based on their proximity and relative orientation. Corners are detected to segment the contour into feature curves. These feature curves are then labeled by checking their geometric properties and relative positions in the neighborhood. Pairs of feature curves are joined by edges if their attributes are compatible (i.e., if they could arise from the same face). The ratios of the feature pairs forming an edge is compared with the golden ratio and a cost is assigned to the edge. If the cost of a group of three feature curves (with different labels) is low, the group becomes a hypothesis. When detecting faces in newspaper articles, collateral information, which indicates the number of persons in the image, is obtained from the caption of the input image to select the best hypotheses BIB002 . Their system reports a detection rate of approximately 70 percent based on a test set of 50 photographs. However, the faces must be upright, unoccluded, and frontal. The same approach has been extended by extracting edges in the wavelet domain by Venkatraman and Govindaraju BIB006 . Tsukamoto et al. presented a qualitative model for face pattern (QMF)  , BIB005 . In QMF, each sample image is divided into a number of blocks, and qualitative features are estimated for each block. To parameterize a face pattern, "lightness" and "edgeness" are defined as the features in this model. Consequently, this blocked template is used to calculate "faceness" at every position of an input image. A face is detected if the faceness measure is above a predefined threshold. Silhouettes have also been used as templates for face localization BIB007 . A set of basis face silhouettes is obtained using principal component analysis (PCA) on face examples in which the silhouette is represented by an array of bits. These eigen-silhouettes are then used with a generalized Hough transform for localization. A localization method based on multiple templates for facial components was proposed in  . Their method defines numerous hypotheses for the possible appearances of facial features. A set of hypotheses for the existence of a face is then defined in terms of the hypotheses for facial components using the DempsterShafer theory  . Given an image, feature detectors compute confidence factors for the existence of facial features. The confidence factors are combined to determine the measures of belief and disbelief about the existence of a face. Their system is able to locate faces in 88 images out of 94 images. Sinha used a small set of spatial image invariants to describe the space of face patterns  ,  . His key insight for designing the invariant is that, while variations in illumination change the individual brightness of different parts of faces (such as eyes, cheeks, and forehead), the relative brightness of these parts remain largely unchanged. Determining pairwise ratios of the brightness of a few such regions and retaining just the "directions" of these ratios (i.e., Is one region brighter or darker than the other?) provides a robust invariant. Thus, observed brightness regularities are encoded as a ratio template which is a coarse spatial template of a face with a few appropriately chosen subregions that roughly correspond to key facial features such as the eyes, cheeks, and forehead. The brightness constraints between facial parts are captured by an appropriate set of pairwise brighter-darker relationships between subregions. A face is located if an image satisfies all the pairwise brighter-darker constraints. The idea of using intensity differences between local adjacent regions has later been extended to a wavelet-based representation for pedestrian, car, and face detection  . Sinha's method has been extended and applied to face localization in an active robot vision system  , BIB008 . Fig. 5 shows the enhanced template with 23 defined relations. These defined relations are furthered classified into 11 essential relations (solid arrows) and 12 confirming relations (dashed arrows). Each arrow in the figure indicates a relation, with the head of the arrow denoting the second region (i.e., the denominator of the ratio). A relation is satisfied for face temple if the ratio between two regions exceeds a threshold and a face is localized if the number of essential and confirming relations exceeds a threshold. A hierarchical template matching method for face detection was proposed by Miao et al. BIB009 . At the first stage, an input image is rotated from À20 to 20 in steps of 5 , in order to handle rotated faces. A multiresolution image hierarchy is formed (See Fig. 1 ) and edges are extracted using the Laplacian operator. The face template consists of the edges produced by six facial components: two eyebrows, two eyes, one nose, and one mouth. Finally, heuristics are applied to determine the existence of a face. Their experimental results show better results in images containing a single face (frontal or rotated) than in images with multiple faces.