A literature survey on robust and efficient eye localization in real-life scenarios <s> Approach <s> Few approaches to automated facial recognition have employed geometric measurement of characteristic features of a human face. Eye spacing measurement has been identified as an important step in achieving this goal. Measurement of spacing has been made by application of the Hough transform technique to detect the instance of a circular shape and of an ellipsoidal shape which approximate the perimeter of the iris and both the perimeter of the sclera and the shape of the region below the eyebrows respectively. Both gradient magnitude and gradient direction were used to handle the noise contaminating the feature space. Results of this application indicate that measurement of the spacing by detection of the iris is the most accurate of these three methods with measurement by detection of the position of the eyebrows the least accurate. However, measurement by detection of the eyebrows' position is the least constrained method. Application of these techniques has led to measurement of a characteristic feature of the human face with sufficient accuracy to merit later inclusion in a full package for automated facial recognition. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Approach <s> 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.<<ETX>> <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Approach <s> Abstract We present a new approach for eye detection using the variance projection function. The variance projection function is developed and employed to locate landmarks of the human eye which are then used to guide the detection of the eye position and shape. A number of eye images are selected to evaluate the capability of the proposed method and the results are encouraging. <s> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Approach <s> The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement. <s> BIB004
Representative works Measuring eye characteristics Shape and intensity contrast Deformable model BIB002 , circular shape of the pupil BIB001 BIB004 Dark eye center BIB003  