Computer Science And Engineering Research Report

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).

Facial recognition is a “one to many” process of searching a large database for individuals having similar features as the probe (Suspect). Facial Recognition is used as a “lead generator only” and should be considered as a tip only, it does not provide a positive identification of an individual. This is similar to inputting a latent print into the Automated Fingerprint Identification System (AFIS) and having ten candidates returned, they are only similar in nature.

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.

Marin Kaste wrote the article “A Look Into Facebook’s Potential To Recognize Anybody’s Face.” This article discusses the challenge of using facial recognition software to determine an individual’s name. The software used for facial recognition has numerous defects, which causes problems when trying to identify an individual using a poor quality photo. Another problem with facial recognition software is if an individual is not in the database, then they cannot be found using the software. The article explains that the problems of facial recognition could be solved by collecting all of the photos of individuals on Facebook to create a universal database.

Out of all of my interests in computer technology, it is also the topic of my graduation thesis. Facial recognition has recently accomplished great achievements since its acknowledgment rate at many institutions has nearly reached 100%, but there are still many obstacles. One of them is the difficulty in recognizing children, understanding the meaning behind the picture and issues with age variation. The performance of most facial recognition algorithms significantly vary with different sample sets. My research is preliminarily and targeted to the aforementioned

Ganetec produces facial recognition systems for business, government, and personal security, however, the systems are capable of not only recognizing an unknown person’s identity but tracking position, and patterns. Ganetec stores all video and picture data for future reference and efficiency of the system, which can be used for analysis of individuals’ decisions. Therefore Ganetec’s servers withhold large quantities of personal information, which may be used for criminal investigations, tracking, or invasion of

I hope this email finds you well! I am a student in your Computer Science 101 lecture (section 001). As the semester is coming to a close I have been thinking about my future in Computer Science (I am currently enrolled in CS201 next semester). Coming into Duke as an athlete, my advisors were hesitant to let me take Computer Science as they thought 1) I wasn’t smart enough and 2) did not have enough time; I had no experience with computer science, but convinced them to let me take it. As I have taken your course, I have grown to love computer science. The idea of being able to intertwine scientific/mathmaatcic principles with personal creativity is so appealing to me. Ideally, I would like to pursue a minor (or even major?) in computer science;

Recognition Rate (RR): This is the amount of the possibility that the system recognizes a face correctly that is among the trained faces or rejects a face not among the trained faces, that is summation of the true positive and true rejection divided by the total number of test attempts. In this work we term the True Positive (TP) as the the correct number of people the system is able to truly identify when the real probe set (44 images) is used and True Rejection is the number of people the system rejected as not identified when any of the unknown images are used. Mathematically, it is calculated as (Chukwuradibia, 2015).

Facial recognition is a technology that recognizes individuals by comparing facial characteristics and specific patterns thus mimicking the function of human brain. It measures the overall facial structure, including distance between eyes, nose, mouth, and jaw edges (1). These measurements are than stored in a database for future use. As the cost of facial recognition software has decreased, its use has grown popular in different fields.

Now: Today, facial recognition technology is being used to identify terriorst, minimize fraud and support law

The process of making an identification based off the method of facial recognition is far more advanced. Facial recognition measures distinguishing facial features such as the dimensions of the nose, the distance between eye sockets, the slope of the forehead, the line and size of the jaw, and the contours of the cheeks (Dewey et al., 2013). Once the subjects photograph is obtained by the specific system, it uses the above parameters to compare the subjects photograph to one which had been previously taken and stored in the organizations

Facial acknowledgment programming is a PC construct program that utilizations focuses in light of still pictures and video pictures on facial components to distinguish a man. It was produced in th 1960s, and is the main semi-robotized framework for facial acknowledgment that required the chairman to find facial elements on photos before it computed separations and proportions to a typical reference indicate that was looked at reference information. (FBI.gov., n.d.) The product works off two methodologies; geometric and photometric; geometric is based off of elements and photometric depends on perspective. Out of the diffrent calculations that were produced, the three, Principal Components Analysis( PCA), Linear Discriminant Analysis (LDA),

Bruce and Young’s theory of recognition tells us that human’s extract several kinds of information from faces; and that there are eight different components of such information. Such as structural encoding, expression analysis, facial speech analysis, directed visual processing, face recognition nodes,

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