Petryshyn T. R.

National Technical University of Ukraine “Kyiv Polytechnic Institute”

Face Detection and Recognition

Face detection is essential front end for a face recognition system. Face detection locates and segments face regions from cluttered images, either obtained from video or still image. It has numerous applications in areas like surveillance and security control systems, content based image retrieval, video conferencing and intelligent human computer interfaces. Most of the current face recognition systems presume that faces are readily available for processing. However, we do not typically get images with just faces. We need a system that will segment faces in cluttered images [2]. With a portable system, we can sometimes ask the user to pose for the face identification task. In addition to creating a more coorperative target, we can interact with the system in order to improve and monitor its detection. With a portable system, detection seems easier. The task of face detection is seemingly trivial for the human brain, yet it still remains a challenging and difficult problem to enable a computer /mobile phone/PDA to do face detection. This is because the human face changes with respect to internal factors like facial expression, beard, mustache glasses etc and it is also affected by external factors like scale, lightning conditions, and contrast between face, background and orientation of face.

Face detection remains an open problem. Many researchers have proposed different methods addressing the problem of face detection. In a recent survey face detection technique is classified in to feature based and image based. The feature based techniques use edge information, skin color, motion and symmetry measures, feature analysis, snakes, deformable templates and point distribution. The problem of face detection in still images is more challenging and difficult when compared to the problem of face detection in video since emotion information can lead to probable regions where face could be located.

Problem definition:

We are given an input scene and a suspect database, the goal is to find a set of possible candidates. We are subject to the constraint that we are able to match the faces from the scene in an interactive time and that our algorithm is able to run on the given embedded hardware.

Approach:

The basic algorithm starts with a pre-processing step, consisting of digitization and segmentation. The next step is called face segmentation. We define the face segmentation problem as: given a scene that may contain one or more faces, create sub-images that crop out individual faces. There are several algorithms available in the literature that can solve this problem. Face segmentation makes use of facial features in order to identify the face. Some algorithms for tracking face contours are known to be effective. Tuning them to our embedded system will be a challenge. After face segmentation, the device enters into the face identification mode, as shown in Figure 2.1.

Figure 2.1 - Face Identification System

Human skin is relatively easy to detect in controlled environments, but detection in uncontrolled settings is still an open problem . Many approaches to face detection are only applicable to static images assumed to contain a single face in a particular part of the image. Additional assumptions are placed on pose, lighting, and facial expression. When confronted with a scene containing an unknown number of faces, at unknown locations, they are prone to high false detection rates and computational inefficiency. Real-world images have many sources of corruption (noise, background activity, and lighting variation) where objects of interest, such as people, may only appear at low resolution. The problem of reliably and efficiently detecting human faces is attracting considerable interest.

Motivation:

Face detection plays an important role in today’s world. They have many real world applications like human/computer interface, surveillance, authentication and video indexing. However research in this field is still young. Face recognition depends heavily on the particular choice of features used by the classifier One usually starts with a given set of features and then attempts to derive an optimal subset (under some criteria) of features leading to high classification performance with the expectation that similar performance can also be displayed on future trials using novel (unseen) test data

Interactive Face Recognition is divided in to several phases; it includes

· Creating drivers for the handheld device that link with the application with the captured image.

· A face detection program is run inside the handheld device which detects the face from the image

· The obtained face is transmitted through wireless network

· The server performs the face recognition and is transmitted back

Each work is assigned three weeks of time.

The Interactive Face Recognition can benefit the areas of: Law Enforcement, Airport Security, Access Control, Driver's Licenses & Passports, Homeland Defense, Customs & Immigration and Scene Analysis. The following paragraphs detail each of these topics, in turn.

Law Enforcement: Today's law enforcement agencies are looking for innovative technologies to help them stay one step ahead of the world's ever-advancing terrorists.

Airport Security: The Interactive Face Recognition device can enhance security efforts already underway at most airports and other major transportation hubs (seaports, train stations, etc.).

Access Control: The Interactive Face Recognition device can enhance security efforts considerably. Biometric identification ensures that a person is who they claim to be, eliminating any worry of someone using illicitly obtained keys or access cards.

Driver's Licenses & Passports: The Interactive Face Recognition device can leverage the existing identification infrastructure. This includes, using existing photo databases and the existing enrollment technology (e.g. cameras and capture stations); and integrate with terrorist watch lists, including regional, national, and international "most-wanted" databases.

Homeland Defense: The Interactive Face Recognition device can help in the war on terrorism, enhancing security efforts. This includes scanning passengers at ports of entry; integrating with CCTV cameras for "out-of-the-ordinary" surveillance of buildings and facilities; and more.

Customs & Immigration: New laws require advanced submission of manifests from planes and ships arriving from abroad; this should enable the system to assist in identification of individuals who should, and should not be there.

The Interactive Face Recognition device is a test bed for embedded face recognition research. As such, it contributes toward building a general infrastructure for research into embedded vision, further benefiting society.

Literature Cited:

[1] Turk and Pentland, Face Reconstruction and Recognition Using Eigen Face

Method.
[2] K.Sandeep and A.N.Rajagopalan, Human Face Recognition in cluttered color
Images using skin color and edge information