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:  Deformable Templates 
1. Abstract of Cited Paper (BIB001):  In the problem area of human facial image processing, the first computational task that needs to be solved is that of detecting a face under arbitrary scene conditions. Although some progress towards this has been reported in the literature, face detection remains a difficult problem. In this paper the authors report on a novel face-finding method that appears quite robust. First, "snakelets" are used to find candidate edges. Candidate ovals (face-locations) are then found from these snakelets using a voting method. For each of these candidate face-locations, the authors use a method introduced previously to find detailed facial features. If a substantial number of the facial features are found successfully, and their positions satisfy ratio-tests for being standard, the procedure positively reports the existence of a face at this location in the image. 
2. Abstract of Cited Paper (BIB002):  The first step for human face recognition is to locate the head boundary in a head-and-shoulders image. An approach that uses adaptive contour models or "snakes" is described to solve this problem. Since we have a priori knowledge of the shape of a head, this active contour model is tailor-made for representing the head boundary. In this paper, a reliable method to locate the approximate position of the head and to estimate the head boundary is proposed. The effect of the parameters for snakes is investigated by locating the head boundary, and a best set of the parameters is suggested. A fast algorithm based on the greedy algorithm for active contour modeling is also presented. The computational complexity of this new algorithm is analyzed and compared with the greedy algorithm. This fast algorithm has a performance capability comparable to the greedy algorithm and reduces the execution time by more than 30% on the average. 
3. Abstract of Cited Paper (BIB003):  Abstract We describe the use of flexible models for representing the shape and grey-level appearance of human faces. These models are controlled by a small number of parameters which can be used to code the overall appearance of a face for image compression and classification purposes. The model parameters control both inter-class and within-class variation. Discriminant analysis techniques are employed to enhance the effect of those parameters affecting inter-class variation, which are useful for classification. We have performed experiments using face images which display considerable variability in 3D viewpoint, lighting and facial expression. We show that good face reconstructions can be obtained using 83 model parameters, and that high recognition rates can be achieved. 
4. Abstract of Cited Paper (BIB004):  We describe a method of locating hypotheses for the positions of faces in an image. We use statistical feature detectors to locate candidates for features, then use a statistical model of the shape and orientation of the features to test combinations of such features to find the most plausible. The best sets can be used as the initial position of an Active Shape Model, which can then accurately locate the full face. 
5. Abstract of Cited Paper (BIB005):  We address the problem of robust face identification in the presence of pose, lighting, and expression variation. Previous approaches to the problem have assumed similar models of variation for each individual, estimated from pooled training data. We describe a method of updating a first order global estimate of identity by learning the class-specific correlation between the estimate and the residual variation during a sequence. This is integrated with an optimal tracking scheme, in which identity variation is decoupled from pose, lighting and expression variation. The method results in robust tracking and a more stable estimate of facial identity under changing conditions. 
6. Abstract of Cited Paper (BIB006):  A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images. > 
7. Abstract of Cited Paper (BIB007):  A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.

Literature Review:
Yuille et al. used deformable templates to model facial features that fit an a priori elastic model to facial features (e.g., eyes) BIB007 . In this approach, facial features are described by parameterized templates. An energy function is defined to link edges, peaks, and valleys in the input image to corresponding parameters in the template. The best fit of the elastic model is found by minimizing an energy function of the parameters. Although their experimental results demonstrate good performance in tracking nonrigid features, one drawback of this approach is that the deformable template must be initialized in the proximity of the object of interest. In BIB002 , a detection method based on snakes BIB008 , BIB001 and templates was developed. An image is first convolved with a blurring filter and then a morphological operator to enhance edges. A modified n-pixel (n is small) snake is used to find and eliminate small curve segments. Each face is approximated by an ellipse and a Hough transform of the remaining snakelets is used to find a dominant ellipse. Thus, sets of four parameters describing the ellipses are obtained and used as candidates for face locations. For each of these candidates, a method similar to the deformable template method BIB007 is used to find detailed features. If a substantial number of the facial features are found and if their proportions satisfy ratio tests based on a face template, a face is considered to be detected. Lam and Yan also used snakes to locate the head boundaries with a greedy algorithm in minimizing the energy function BIB003 . Lanitis et al. described a face representation method with both shape and intensity information BIB004 . They start with sets of training images in which sampled contours such as the eye boundary, nose, chin/cheek are manually labeled, and a vector of sample points is used to represent shape. They used a point distribution model (PDM) to characterize the shape vectors over an ensemble of individuals, and an approach similar to Kirby and Sirovich  to represent shapenormalized intensity appearance. A face-shape PDM can be used to locate faces in new images by using active shape model (ASM) search to estimate the face location and shape parameters. The face patch is then deformed to the average shape, and intensity parameters are extracted. The shape and intensity parameters can be used together for classification. Cootes and Taylor applied a similar approach to localize a face in an image BIB005 . First, they define rectangular regions of the image containing instances of the feature of interest. Factor analysis  is then applied to fit these training features and obtain a distribution function. Candidate features are determined if the probabilistic measures are above a threshold and are verified using the ASM. After training this method with 40 images, it is able to locate 35 faces in 40 test images. The ASM approach has also been extended with two Kalman filters to estimate the shape-free intensity parameters and to track faces in image sequences BIB006 .