No previous work has been reported on any aspects of similarity recognition in images of family faces. However, it is appropriate to review research on face recognition, as many of the issues encountered in our problem are similar to those encountered in related problems. Face recognition systems has been carried out in two distinct paradigms. In the first paradigm researchers first extract facial features such as the eyes, nose, etc., then they utilize clustering/classification algorithms to recognition. The second paradigm treats the complete face image as an input vector and bases analysis and recognition on algebraic transformations of the input space. The current research has adopted these two paradigms for family similarity recognition.
Face recognition algorithms generally have three phases, including Feature Extraction phase (reducing the size of test images), Learning phase (clustering/classification) and Recognition phase. It can be claimed that the main difference between all methods, which are proposed by researchers in last three decades, is in feature extraction stage. Superior efforts have been carried out for the feature extraction, and the Principal Component Analysis (PCA) family algorithms are the most popular algorithm for reducing the problem space size that might have been used.
Turk and Pentland [] are used PCA in face recognition for the first time. The feature vectors for PCA are vectorized face images. PCA rotates feature vectors from a large and highly correlated subspace to a small subspace which basis vectors correspond to the maximum variance direction in the original image space. This subspace is called Eigenface, which useless information such as lighting variations or noise is truncated and the ...
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...the first who use the wavelet transform with Haar filters to extract 16 images from the original image. The mean and standard deviation of each image form the feature vector. In recognition stage, the Bhattacharyya distance is utilized to find the distance between the feature vector of input image and feature vectors of obtained subspace. Kinage and Bhirud [Kin09] extend this study, and use two-dimensional wavelet transform plus 2DPCA. First, a wavelet transform applied on image to obtain a reduced size and insensitive to illumination one. Then, 2DPCA clustering method is used to extract the feature space. In recognition step, the Euclidean distance between input image and experimental samples is calculated to find out the class, which the input image is belonged to. Experiments in AT&T face database shows that the success rate of the proposed method is 94.4 percent.
A study was conducted to see people’s reactions to angry and sad faces of men and women. When these two faces were blended together, as in, the angry woman and sad woman were blended...
...means and become familiar with K-means clustering and its usage. Then, we finish this part by different method of clustering. The K-nearest- neighbors is also discussed in this chapter. The KNN is simple for implication, programming, and one of the oldest techniques of data clustering as well. There are many applications existing for KNN and it is still growing. The PCA also discussed in this chapter as a method for dimension reduction, and then discrete wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
“In every conceivable manner, the family is link to our past, and bridge to our future” (Haley*). In the genogram presented, I was able to identify many characteristics, traits, behaviors, individual psychological features and even secrets about my family. My genogram is composed of 4 generations, beginning with grandparents from both sides, the middle sections of the genogram include my mother and father’s brothers and sisters, the second to last layer is their children, which is me and my cousins, and ending the genogram are the newborn babies that my cousins Talibah Alfred, and Mashay Hackshaw will be having February 2017 .
The most predominant feature of the human face is eyes. When talking to a person our eyes meet there eyes; the way that people identify each other is through eyes; eyes even have the power to communicate on its own. Eliezer identified people buy there eyes and knew their emotions through their eyes. “Across the aisle, a beautiful women with dark hair and dreamy eyes. I had
What is the first thing image that comes to mind when somebody says education? Education and learning can be depicted in an almost infinite number of ways, in "Scenes and Un-Scenes: Looking at Learning", education is depicted in few completely different ways, from a classroom full of children to a woman home schooling a group of small and older children. This brings upon the idea that there is no set idea when it comes to education and learning, that everything is experienced as an individual. Learning and education have such a huge impact on human development and life in general.
Biometrics is described as the use of human physical features to verify identity and has been in use since the beginning of recorded history. Only recently, biometrics has been used in today’s high-tech society for the prevention of identity theft. In this paper, we will be understanding biometrics, exploring the history of biometrics, examples of today’s current technology and where biometrics are expected to go in the future.
Whoever sees this picture, it would be considered as a family group image. However, something makes me feel strange when I look at it first because they do not look familiar one another at all. Perhaps, there are some difficulties going on among these family members according to their troubled facial emotions and poses, like they do not look at each other or their inflexible postures. Even though audiences view this picture as a group of family members, they would find out that this family has some horrible issues on. This picture is enough to attract a number of audiences’ interests and it makes them think critically about the problems in family. For a few families, occasions are only one more reason to get together to consume great foods and to have a decent time. They are not searching for articles like this one on the grounds that they have by one means or another evaluated the equation for fruitful family fellowship with least push.
Biometrics-based authentication applications include workstation, network, and domain access, single sign-on, application logon, data protection, remote access to resources, transaction security and Web security (Campbell, 1995). Utilized alone or integrated with other technologies such as smart cards, encryption keys and digital signatures, biometrics are set to pervade nearly all aspects of the economy and our daily lives (Campbell, 1995). Among the features measured are; face, fingerprints, hand geometry, iris, and voice (Campbell, 1995).
When Maxwell Smart first whipped out his shoe phone in 1965, everyone saw an act of pure movie magic. Back in the mid to late 1900s everybody had the same idea of the future. Everyone pictured the future as talking robots (Siri), computerized pocket-sized dictionaries (smart-phones), hovering devices (drones), and much more. Today, everyone thinks of these technologies as commonalities. Most of these current devices have a valuable impact, while few create debatable issues. The company NGI has a system that will revolutionize the field of biometric facial recognition. In the article titled Embracing Big Brother: How Facial Recognition Could Help Fight Crime, author Jim Stenman says, "The mission is to reduce terrorist and criminal activity by improving and expanding biometric identification as well as criminal history information s...
The term biometrics is commonly known as the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Though, even more recently it has taken on a whole new definition. Biometrics is an amazing new topic referring to “the emerging field of technology devoted to the identification of individuals using biological traits, based on retinal or iris scanning, fingerprints, or face recognition”. Biometrics has already begun using applications that range from attendance tracking with a time clock to security checkpoints with a large volume of people. The growing field of biometrics has really been put on the map by two things, the technological advances made within the last 20 years, and the growing risk of security and terrorism among people all over the world. In this paper I will focus on: the growing field of biometrics, why it is important to our future, how the United States government has played a role in its development and use, the risks involved, the implications on public privacy, and further recommendations received from all over the science and technology field.
everyone since teh beginning fo time has had their own views and standards for the way that everything around them should be. these views are seemingly set in stone and unchangeable. there are many examples in the past of terrible consequences for expressing views other than the norm at the time. more recently this apprehension to change was described by Thomas Kuhn in his book, The Structure of Scientific Revoulutions.
“Computers play a key role in almost every sphere of life” (Berry, Terrie). Without them, everything would be different. People all around the world own or used a computer. Whether you are a professional in technology or just an average person, computers are still very important in your everyday life. “No other technology has accessed the world like computers and the use of the Internet have” (Maddox, Amanda). Without computers, consumers would not be able to do half the things as quickly as we can. Advances in computer design have increased its use for different industries, especially in the medical field.
asteroid was on a line with Earth, the computer would show us and enable us
Each of the three learning theories, Cognitivism, Constructivism, and Behaviorism, has worth and merit in my opinion. Yet, each one has its own unique qualities with one common factor, the learning process. It seems to me that the best teacher is one who would utilize all the theories of learning. However, if I look closely, I am most likely favoring one or two more than the others in my own instructional methods. I read the brief definition of these three theories and realized that I needed to examine a more in-depth explanation of each of them. The theory of cognitivism focuses on the mind of the learner
Iris recognition is very accurate and distinctive because iris has a complex texture that can produce a substantial amount of information to identify a person. Furthermore, the iris remains almost unchanged from childhood, only minuscule variations are presented. The biometric data is captured using a small and high definition camera that is able to recognize different characteristics of the iris. Moreover, the system can detect the use of contact lens with a fake iris and can realize with the natural movement of the eye if the sample object is a living being. Although initially iris recognition systems were expensive and complex to use, new technology developments have improved these weaknesses.