Face Recognition Essay

Many of the works of C.S. Lewis contain themes of Christianity and the relationship between man and the spiritual world. Written in the mid 1950s and towards the end of his life, Till We Have Faces is one of Lewis’ most intellectual works. Considered as his most mature work of writing, it is a retelling of the classical mythical story of Cupid and Psyche from the perspective of Orual, the older sister of Psyche. It prompts the reader to relate with Orual as the victim of the “gods,” then, when the veil is lifted and the faults of Orual are revealed, the reader is able to better recognize the faults of his own and those of society. Till We Have Faces opens the eyes of readers to what love should be, and to its effect upon the world.

Critically reflect on the positives and/or negatives of ethnic residential concentration as perceptible within specific landscapes in Sydney.

Biometrics technology aims at utilizing major and distinctive characteristics such as behavioral or biological, for the sake of positively indentifying people. With the help of a combination of hardware and specific identifying sets of rules, a basic human attribute, automated biometric recognition mimics to distinguish and categorize other people as individual and unique. But the challenges surrounding biometrics are great as well.

The digital image of the face of a person can be matched against a record of other images by using face recognition software. In case that some of the images in the database go with the digitized image, the owner is reported about it by the system. Automatic face recognition has been studied and investigated extensively since 1990s and its usage is turning out to be no-nonsense in the present times. Although a number of automatic face recognition applications are relatively agreeable and harmless (access regulation to armaments, currency, illegal proof, nuclear equipment/supplies etc), there are still several dangers of face recognition systems that include violation of privacy and civil liberties, unrestrained exploitation, identity theft, illegal use of database, free access to personal data on social media etc. (Agre, 2001).

Biometrics is a method of identifying an individual based on characteristics that they possess, typically physiological features such as a fingerprint, hand, iris, retina, face, voice, and even DNA. Some methods of biometrics security even use multiple physiological features or multimodal biometrics to provide superior security than a single form of biometrics can provide. Why are biometrics important in the field of information security? Biometrics provide a remarkable amount of security for information because biometrics are unique to each person, and thus cannot be lost, copied, or shared with another individual. This security allows for biometrics to provide a means to reliability authenticate personnel. The importance of biometrics can be further divided into the history of biometrics and why it was devised, past implementations of biometrics, current implementations of biometrics, and future implementations of biometrics.

In the past years law enforcement profession has been in the headlines of numerous newspapers and news station concerning their conduct in public. From the Freddie Gray incident in Baltimore Maryland, Eric Garner in Staten Island, to Ferguson shooting with Michael Brown. All these cases had certain circumstances leading up to the incident, however the public, especially the African-American community wants more oversight of the police departments. The issue in law enforcement within these communities has been the amount of force the officers are using and the lack of transpartity within the agencies. These issues with the police departments and the African-American community has created tension

Now it may seem frightening to hear that the NSA has the capability to access privacies such as emails and bank accounts which leads many to the question of how is searching these things going to protect their safety? This concern is genuine, but it helps to compare it to something more well known. Airport security is something we all have to endure, and we do not have the option to opt out of having our belongings and ourselves be checked by TSA agents. It may seem unnecessary to check every single passenger traveling through an airport, but when it comes to the safety of all of the passengers on a plane, it is widely agreed to be worth it. Although not always publicly known, the TSA stops many people with prohibited items. It is

Face recognition is another biometric technology. Face recognition uses the same technology that iris recognition uses. For face recognition, a camera takes several images of a person to find out who it is. Face recognition differs from all of the other biometric technologies because the person that is in the picture does not have to cooperate with the process. In all of the other technologies it requires the people to actively participate in the process. In face recognition, the image can be taken without the person even knowing that it took place.

Through this routine of advanced technology analysis, it has been established to increase the results and have hastened the procedure of identifying suspects of crimes. Facial recognition is also necessary for public involvement and observation as it also aids law enforcement officials to more easily zone in on possible suspects of a crimes being caught. With the use of facial recognition, it constantly has been proven quite an effective method with the incorporation of this technique.

Automatic face recognition has always been a major focus of research for a few decades, because of numerous practical applications where human identification is needed. Compared to other methods of identification (such as fingerprints, voice, footprint, or retina), face recognition has the advantage of its non-invasive and user friendly nature. Face images can be captured from a distance without interacting with the person, which is particularly beneficial for security and surveillance purposes. Furthermore, extra personal information, like gender, face expressions or age, can be obtained by further analyzing recognition results. Nowadays, face recognition technology has been widely applied to public security, person verification, Internet

Abstract— In this paper, an approach for gender classification is carried out combining frontal face images, Haar cascades, Histogram of Oriented Gradients and Support Vector Machines. The comparison of the existing methods that delves into the effects of Haar Cascade Classifier and Histogram of Oriented Gradients(HOG) for Face Detection and the use of Support Vector Machines(SVM) for Gender Classification. A database of 2-D facial images was used, consisting of individual as well as group photographs. These images were used in face detection by Haar classifier and HOG and the results were compared at the end. Further, the detected faces were used to extract primary features on a face, such as, nose, eyes, lips, chin. Feature Extraction is used to calculate ratios, which differ for a Male and a Female. SVM uses these ratios in training and therefore classifying gender. Success ratio for HOG is found to be more than Haar Classifier and high accuracy is achievable with SVM.

Brar and Brar [1] have presented that the image safety on internet transfers is the priority of the hour as the breaching assaults into the photograph databases are growing every year. The hackers take benefit of the stolen non-public and crucial pictures to meet their dangerous and unethical intentions. The photograph statistics theft can be used to defame someone at the internet by means of posting the illegal and unacceptable photographs of that individual (internet user. Majority of the prevailing image safety systems are not up to date to guard in opposition to the cutting-edge breaching attacks. A hybrid photograph security framework has been proposed to conquer the problem stated earlier, a good way to be carried out by means of

Biometric technologies are getting better and finely tuned. The rate of false readings and errors has sharply fallen. However it still requires careful consideration and planning to implement a biometric identification system. They are most costly and complicated to implement as compared with other authentication systems. A proper evaluation of the system is important before purchasing any biometric system. A thorough risk analysis is necessary. In many cases biometrics may be overkill. Biometrics must be used if there is high level of risk involved. Customer acceptance is also important when logging on to company websites. Home users might not be ready to install biometrics on home computers for online banking.

Abstract: In this paper a hybrid technique is used for determining the face from an image. Face detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP) formula, which aims at finding a linear approximation to the Eigen functions of the astronomer Beltrami operator on the face manifold. However, LPP is non-orthogonal and this makes it difficult to reconstruct the information. When the set of features is found by the OLPP, with the help of the PSO, the grouping of the image features is done and the one with the best match from the database is given as the result. This hybrid technique gives a higher accuracy in less processing time.

Vital challenge in the real time applications is on the face recognition through different illumination. Despite of numerous illuminations filtering techniques are in place, the concepts are said to be archaic and the critical analysis of performance of illumination filtering techniques are not covered [1, 2]. Based on the current face recognition tech-niques that come with a group function to normalize the illumination help to facilitate the critical challenge in the face recognition system [3]. Most of the techniques used in photometric normalization were used to develop the methods used in this particular toolbox [4]. Those are the techniques used for carrying out the normalization of illumi-nation prior to the actual processing stage. One could see that it is in contrary with the methods applied in compensating the appearances induced by illumination at the classi-fication or modelling stage [5, 6].