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
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 Strategies Face detection techniques and algorithm deals with detection of face in clutter background with poses. True positives (detection rate) and false positives (detections of non face regions) would have to be very high and very low respectively for an ideal