Examples Of Face Recognition

Face Recognition, as the most successful applications of image analysis and understanding, has recently received significant attention. Recognition implies the tasks of identification or authentication. Identification involves a one-to-many comparison to fetch unknown identity from a set of known possibilities. Authentication involves a one-to-one comparison to verify a claimed identity. Furthermore, closely related to recognition is classification where the problem is to identify a group of individuals as sharing some common features. Their applications include security monitoring, automated surveillance systems, access control, mug shot identification, suspect versus perpetrator verification, facial reconstruction, victim and missing person

In any system, challenges are race, age, gender, facial expression, or speech may be used in narrowing the search. In order to solve this problem, segmentation of faces (face detection) from cluttered scenes, feature extraction from the face regions, recognition, or verification is used. In identification, the input to the system is an unknown face, and the system reports back the determined identity from a database, whereas in verification problems, the system needs to confirm or reject the claimed identity. The first step in any automatic face recognition systems is the detection of faces in images. After a face has been detected, the task of feature extraction is to obtain features that are fed into a face classification system. Depending on the type of classification system, features can be local features such as lines or facial features such as eyes, nose, and mouth. Face detection may also employ features, in which case features are extracted simultaneously with face detection. Feature extraction is also a key to animation and recognition of facial expressions.

Face Detection

Snakes used to create a head boundary. They lock on to nearby edges, assuming the shape of the head. To achieve it energy function are minimized, which consists of the sum of an internal energy function, defining its natural evolution and an external energy function, which counteracts the internal energy enabling the contours to deviate from the natural evolution. Deformable Templates is an extension to the snake models. Yuille et al [5] used global information of the eye to improve the extraction process. Once established near an eye feature, optimal feature boundaries are minimized using steepest gradient descent minimization. Its Limitations is that it is sensitive to initial placement and the processing time is high.
Smart Snakes or Point Distributed Models (PDMs) are compact parameterized descriptions of a shape based upon statistics. They use PCA to construct a linear flexible model from variations of the features in a training set. Face PDM has 95% detection