A literature survey on robust and efficient eye localization in real-life scenarios <s> The global system architecture for eye localization <s> In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> The global system architecture for eye localization <s> The Automatic Facial Expression Recognition has been one of the latest research topic since 1990’s.There have been recent advances in detecting face, facial expression recognition and classification. There are multiple methods devised for facial feature extraction which helps in identifying face and facial expressions. This paper surveys some of the published work since 2003 till date. Various methods are analysed to identify the Facial expression. The Paper also discusses about the facial parameterization using Facial Action Coding System(FACS) action units and the methods which recognizes the action units parameters using facial expression data that are extracted. Various kinds of facial expressions are present in human face which can be identified based on their geometric features, appearance features and hybrid features . The two basic concepts of extracting features are based on facial deformation and facial motion. This article also identifies the techniques based on the characteristics of expressions and classifies the suitable methods that can be implemented. <s> BIB002
Due to the complexity of the eye localization, it is better to use a divide and conquer strategy to handle different variations at different stages. Inspired by BIB002 , we give a global architecture for eye localization (see Fig. 11 ). Note that this is only to illustrate the general pipeline for an eye localization task and it is possible to instantiate different systems according to this architecture but not all components are mandatory in a practical system. For example, the pictorial structure model BIB001 and ASM model  do not rely on a careful face normalization to build their shape model. As shown in Fig. 11 , the architecture is roughly divided into three components, i.e., face processing, feature extraction and representation for eye model, and eye localization. The face processing component can be thought of as a preprocessing stage for eye feature extraction. Meanwhile, the problem of the accuracy and efficiency tradeoff is essential for any practical eye localization system. Finally, before the locations of the eyes are actually outputted, some postprocessing which combines multiple searching results of eye detectors needs to be done properly. Next we will have a closer look at these issues.