Need For Face Recognition Emergence

Published Date: 02 Nov 2017

1. INTRODUCTION:

Face recognition is the task of identifying an already detected object as a KNOWN or UNKNOWN face, and in more advanced cases, telling EXACTLY WHO'S face it is!. Face recognition is an easy task for humans. Experiments [1] have shown, that even one to three day old babies are able to distinguish between known faces. So how hard could it be for a computer? It turns out we know little about human recognition to date. Are inner features (eyes, nose, mouth) or outer features (head shape, hairline) used for a successful face recognition? How do we analyze an image and how does the brain encode it?. Our brain has specialized nerve cells responding to specific local features of a scene, such as lines, edges, angles or movement. Since we don’t see the world as scattered pieces, our visual cortex must somehow combine the different sources of information into useful patterns. Automatic face recognition is all about extracting those meaningful features from an image, putting them into a useful representation and performing some kind of classification on them.Photobucket

In Face recognition process, it can pick someone's face out of a crowd, extract the face from the rest of the scene and compare it to a database of stored images. In order for this software to work, it has to know how to differentiate between a basic face and the rest of the background. Facial recognition software is based on the ability to recognize a face and then measure the various features of the face.

The system goes through a series of steps to verify the identity of an individual.

1. Detection: Acquiring an image can be accomplished by digitally scanning an existing photograph (2D) or by using a video image to acquire a live picture of a subject (3D).

2. Alignment: Once it detects a face, the system determines the head's position, size and pose. As stated earlier, the subject has the potential to be recognized up to 90 degrees, while with 2D, the head must be turned at least 35 degrees toward the camera.

3. Measurement: The system then measures the curves of the face on a sub-millimeter (or microwave) scale and creates a template.

4. Representation: The system translates the template into a unique code. This coding gives each template a set of numbers to represent the features on a subject's face.

5. Matching: If the image is 3D and the database contains 3D images, then matching will take place without any changes being made to the image. However, there is a challenge currently facing databases that are still in 2D images. 3D provides a live, moving variable subject being compared to a flat, stable image. New technology is addressing this challenge. When a 3D image is taken, different points (usually three) are identified. For example, the outside of the eye, the inside of the eye and the tip of the nose will be pulled out and measured. Once those measurements are in place, an algorithm (step-by-step procedure) will be applied to the image to convert it to a 2D image. After conversion, the software will then compare the image with the 2D images in the database to find a potential match.

6. Recognition or Identification: In verification, an image is matched to only one image in the database (1:1). For example, an image taken of a subject may be matched to an image in the Department of Motor Vehicles database to verify the subject is who he says he is. If identification is the goal, then the image is compared to all images in the database resulting in a score for each potential match (1:N). In this instance, you may take an image and compare it to a database of mug shots to identify who the subject is.

However completing these steps is not that easy. There are few challenges for any biometric face recognition systems[2]: (1)During identification, the system has to operate with a large dataset and must identify a match quickly, (2) An identification system needs an efficient searching and matching algorithm, and (3) The number of false-positives in the system should be fewer as the size of the database increases.

2. NEED FOR FACE RECOGNITION EMERGENCE

Positive identification of individuals is a very basic societal requirement. In small tribes and villages, everyone knew and recognized everyone else. You could easily detect a stranger or identify a potential breach of security. In today's larger, more complex society, it isn't that simple. In fact, as more interactions take place electronically, it becomes even more important to have an electronic verification of a person's identity. Until recently, electronic verification took one of two forms: 1- It was based on something the person had in their possession, like a magnetic swipe card or 2- something they knew, like a password.

The problem is, these forms of electronic identification aren't very secure, because they can be given away, taken away, or lost and motivated people have found ways to forge or circumvent these credentials. So, the ultimate form of electronic verification of a person's identity is biometrics; using a physical attribute of the person to make a positive identification. There are many robust biometric techniques like fingerprinting which can be used for human authentication then why go for face recognition?

In many applications like the surveillance and monitoring ,say, of a public place, the traditional biometric techniques will fail as for obvious reasons we can not ask everyone to come and put his/her thumb on a slide or something similar. So we need a system which is similar to the human eye in some sense to identify a person. To cater this need and using the observations of human psychophysics, face recognition as a field emerged.

Different approaches have been tried by several groups, working world wide, to solve this problem. Many commercial products have also found their way into the market using one or the other technique. But so far no system / technique exists which has shown satisfactory results in all circumstances.

3.FACE RECOGNITION TECHNIQUES

Face recognition methods are fall into two main categories: feature-based and appearance based methods. An overview of some of the well-known methods in these categories is given below.

3.1 Featured-based Methods:

Feature-based approaches[3-5] first process the input image to identify and extract (and measure) distinctive facial features such as the eyes, mouth, nose, etc., as well as other fiducial marks, and then compute the geometric relationships among those facial points, thus reducing the input facial image to a vector of geometric features. Standard statistical pattern recognition techniques are then employed to match faces using these measurements. Early work carried out on automated face recognition was mostly based on these techniques. One of the well-known feature-based approach is the elastic bunch graph matching method[10].

The main advantage offered by the featured-based techniques is that since the extraction of the feature points precedes the analysis done for matching the image to that of a known individual, such methods are relatively robust to position variations in the input image[6]. In principle, feature-based schemes can be made invariant to size, orientation and/or lighting. Other benefits of these schemes include the compactness of representation of the face images and high speed matching .The major disadvantage of these approaches is the difficulty of automatic feature detection and the fact that the implementer of any of these techniques has to make arbitrary decisions about which features are important.

3.2 Appearance-based Methods:

Appearance based methods approaches attempt to identify faces using global representations, i.e., descriptions based on the entire image rather than on local features of the face[6]. Techniques such as Eigen faces, Principal component analysis (PCA) , linear discriminate analysis (LDA) and independent component analysis (ICA) have demonstrated the power of appearance based methods both in ease of implementation and in accuracy. Here the feature vector used for classification is a linear projection of the face image into a low-dimensional linear subspace. In extreme cases, the feature vector is chosen as the entire image, with each element in feature vector taken from a pixel in the image.

The main advantage of the Appearance-based approaches is that they do not destroy any of the information in the images by concentrating on only limited regions or points of interest[6]. However, as mentioned above, this same property is their greatest drawback, too, since most of these approaches start out with the basic assumption that all the pixels in the image are equally important[7].

Consequently ,these techniques are not only computationally expensive but require a high degree of correlation between the test and training images, and do not perform effectively under large variations in pose, scale and illumination, etc.. Despite their success, the error rates of existing appearance based face recognition systems are too high for many of the applications in mind.  The reasons for these high error rates stem from a number of sub-problems.

Face recognition systems are highly sensitive to the environmental circumstances under which the images being compared are captured.  In particular, changes in lighting conditions can increase both false rejection rates (FRR) and false acceptance rates (FAR).

Another problem is associated with facial orientation and angle of image capture.  When standard 2D intensity images are used, in order to achieve low error rates, it is necessary to maintain a consistent facial orientation (preferably a frontal parallel perspective) for both the query image and gallery image.  Even small changes in facial orientation can reduce the effectiveness of the system.

This situation is worsened by the fact that people’s facial expressions can change from one image to another, increasing the chance of a false rejection.  The result is that in order to produce secure site access systems, it is necessary to specify a required facial expression (usually neutral).  However, this approach then removes one of the key advantages of face recognition i.e. no need for subject co-operation, rendering such a system unsuitable for surveillance applications.

Nevertheless, as mentioned in the above review, several of these algorithms have been modified and/or enhanced to compensate for such variations, and dimensionality reduction techniques have been exploited (note that even though such techniques increase generalization capabilities, the downside is that they may potentially cause the loss of discriminative information, as a result of which these approaches appear to produce better recognition results than the feature-based ones in general.

In the latest comprehensive FERET evaluation, the probabilistic eigenface , the Fisherface and the EBGM methods were ranked as the best three techniques for face recognition (Even though the EBGM method is feature-based in general, its success depends on its application of holistic neural network methods at the feature level).

4. THREE DIMENSIONAL FACE RECOGNITION:

In the earlier period, face recognition system has relied on a 2D image to evaluate or identify another 2D image from the database. To be successful and precise, the image captured needed to be of a face that was looking almost directly at the camera, with little inconsistency of light or facial expression from the image in the database and this created problems. In most cases the images were not taken in a controlled situation. Even the smallest changes in light or orientation could diminish the effectiveness of the system, so they could not be matched to any face in the database, leading to a high rate of failure.

A newly emerging trend, claimed to achieve improved accuracies, is three-dimensional face recognition. The motivation to use 3D technology was to overcome the disadvantages of 2D face recognition systems that arise especially from significant pose, expression and illumination differences.

This technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin. One advantage of 3D facial recognition is that it is not affected by changes in lighting like other techniques. It can also identify a face from a range of viewing angles, including a profile view. Three-dimensional data points from a face vastly improve the precision of facial recognition. 3D research is enhanced by the development of sophisticated sensors that do a better job of capturing 3D face imagery.

5. CHALLENGES AHEAD

Identifying people in uncontrolled environments still presents the biggest challenges in facial recognition reliability, but the technology has made tremendous strides in just the past few years, according to researchers.

The biggest technology challenges that remain in facial identification technology are overcoming low-resolution facial images, occlusion, orientation (being able to recognize equally profiles and frontal face), and orientation age -- mainly very young and very old

Perfecting face recognition technology is dependent on being able to analyze multiple variables, including lighting, image resolution, uncontrolled illumination environments, scale, orientation (in-plane rotation), pose (out-of-plane rotation), people's expressions and gestures, aging, and occlusion (partial hiding of features by clothing, shadows, obstructions, etc.).This is highly challenging for computer scientists. Solutions are largely mathematical, with new procedural and machine-learning algorithms being developed to improve accuracy.

Researchers are also looking at ways to apply the latest advances in facial-recognition technology to uncontrolled environments, where success rates are 50 percent or lower. Improving success rates in video and film.

6. APPLICATIONS

6.1 Law enforcement and justice solutions:

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

• As such, FRS is committed to developing technologies that can make the jobs of the law enforcement officer easier. This includes acclaimed CABS-computerized arrest and booking system and the childbase protection, a software solution for global law enforcement agencies to help protect and recover missing and sexually exploited children, particularly as it relates to child pornography.

6.1.1 CABS:

• Store all offence-related detain one easy-to-use system -- data is entered once and only once.

• Integrate with any database -- including other detachments and other applications (RMS, CAD, Jail Management systems, and "most-wanted" databases) .

• Link victims to offenders -- to aid in criminal analysis and investigations

• Capture and store digital images of the offender -- encode all mug shots, marks, tattoos, and scars

• Perform rapid and accurate searches -- on all data and image fields for crime statistics and reporting

• Produce digital lineups -- using any stored image in minutes

• Identify previous offenders -- pre-integrated with advanced biometric face recognition software.

6.1.2  Child base protection:

• ChildBase is an application that helps protect and recover missing and sexually-exploited children, particularly those children victimized through child abuse images.

6.2 Identification solutions:

With regards to primary identification documents, (Passports, Driver's licenses, and ID Cards), the use of face recognition for identification programs has several advantages over other biometric technologies.

• Leverage your existing identification infrastructure. This includes, using existing photo databases and the existing enrollment technology (e.g. cameras and capture stations); and

• Increase the public's cooperation by using a process (taking a picture of one's face) that is already accepted and expected;

• Integrate with terrorist watch lists, including regional, national, and international "most-wanted" databases.

6.3  Homeland defense:

• Since the terrorist events of September 11, 2001, the world has paid much more attention to the idea of Homeland Defense, and both governments and private industries alike are committed to the cause of national defense.• This includes everything from preventing terrorists from boarding aircraft, to protecting critical infrastructure from attack or tampering (e.g. dams, bridges, water reservoirs, energy plants, etc.), to the identification of known terrorists.

6.4 Airport security:

• Airport and other transportation terminal security is not a new thing. People have long had to pass through metal detectors before they boarded a plane, been subject to questioning by security personnel, and restricted from entering "secure" areas. What has changed, is the vigilance in which these security efforts are being applied.

• The use of biometric identification,  can enhance security efforts already underway at most airports and other major transportation hubs (seaports, train stations, etc.).

• This includes the identification of known terrorists before they get onto an airplane or into a secure location.

6.5 Immigration:

• Most countries do not want to be perceived as being a "weak link" when it comes to accepting immigrants and refugees, particularly if that individual uses the new country as a staging ground for multi-national criminal and terrorist activities. Consequently, governments around the world are examining their immigration policies and procedures.

• Biometric technology, particularly face recognition software, can enhance the effectiveness of immigration and customs personnel. After all, to the human eye it is often difficult to determine a person's identity by looking at a photo, especially if the person has aged, is of a different ethnic background, has altered their hair style, shaved their beard, etc. FRS does not have this difficulty.

6.6 Access control:

• The use of biometric technology, particularly face recognition software (either independently or as one part of a multi-layered biometric solution), can enhance your 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.

6.7 Financial services:

• The financial services industry revolves around the concept of security. Yet for the most part, security within the industry is limited to a simple personal identification number (PIN) or password.

• Biometrics, particularly face recognition software, can improve the security of the financial services industry, saving the institution time and money both through a reduction of fraud cases and the administration expenses of dealing with forgotten passwords.

• Furthermore, biometric-based access control units can safeguard vaults, teller areas, and safety deposit boxes to protect against theft.

• The use of biometrics can also ensure that confidential information remains confidential while deterring identity theft, particularly as it relates to ATM terminals and card-not-present e-commerce transactions. 

6.8 Scene analysis and surveillance solutions:

• This includes the ability to extract, categorize, and search non-facial imagery. For example, within the law enforcement application it allows you to capture, archive, and retrieve such identifying characteristics as tattoos, marks, or scars.

• It can also analyse scenes from either streaming or archived video, "looking" for out-of-the-ordinary occurrences, the presence of certain vehicles, specific faces, etc.

• This is beneficial and can save significant time and money to those individuals who spend hours, days, or weeks monitoring video streams (i.e. examining a bank's security in a criminal investigation).

7. CONCLUSION

Face recognition is a challenging problem in the field of image analysis and computer vision that has received a great deal of attention over the last few years because of its many applications in various domains. Research has been conducted vigorously in this area for the past four decades or so, and though huge progress has been made, encouraging results have been obtained and current face recognition systems have reached a certain degree of maturity when operating under constrained conditions; however, they are far from achieving the ideal of being able to perform adequately in all the various situations that are commonly encountered by applications utilizing these techniques in practical life.This paper presented an extensive review of face recognition .Also focused on face recognition Techniques its advantages and disadvantages, challenges and its applications.