In this paper, a new algorithm for age group recognition from frontal face image is presented. The algorithm classifies subjects into four different age categories in four key stages: Pre-processing, Facial feature extraction by a new projection method, Face feature analysis, and Age classification. In order to apply the algorithm to the problem, a face image database focusing on people’s age information is required. Because there are no such these databases, we created a database for this purpose, which is called Iranian Face Database (IFDB). IFDB contains digital images of people between 1 and 85 years old. After pre-processing, primary features of the faces in the database are accurately detected. The rest of the stages employ a neural network to classify the face into age groups using computed facial feature ratios and wrinkle densities. The experimental result shows that the algorithm identifies the age group with accuracy of 86.64%. Keywords: Age Group Recognition; Facial Feature Extraction; Wrinkle Analysis; Artificial Neural Network; Face Image Database. 1. Introduction Facial image processing is a research context, which has been studied by many researchers in recent decades. Face Recognition [1, 18], Facial Expression [9], Gender Classification [4], Face Detection [27], and Facial Feature Extraction [] are results of research in this area. Age group classification from facial images is one of the applications despite the theoretical and practical importance has been not considered adequately. This shortcoming is due to three reasons include (I) increasing in the number of classes, (II) inaccuracy even by human evaluations, and (III) non existence of a proper large data set. Age related research have been considered... ... middle of paper ... ... be discussed in Section 2.6. VPF’, MIPF’ are formulated as follows: Another contribution of this study is utilizing Principal Component Analysis (PCA) as a feature localizer. PCA involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Usually PCA applies to a homogenous data set. In this paper, we assume that an image, itself, composed of a data collection which lies together in different rows. Therefore, PCA is applied on an image and the first and second principal components are extracted. Analysis of the extracted components helps locate facial features precisely.
This advertisement illustrates ageism by saying a “younger, hotter airline” is better. So in order for something to be better than something else it has to be “younger and hotter?” This advertisement might make older people feel bad about being the age they are, and it might lower their self-image. Self-image, especially for women, is really focused on. Some advertisements like http://www.realbodystory.com/img_client/ageist_ex1.jpg and http://www.ltcconline.net/lukas/gender/ageism/pics/ageism2.jpg show that wrinkles are not okay and it also not okay to look like one’s mother. Again, ageism is portrayed here by showing that wrinkles should be eliminated and that someone has to keep up with how they look in order to not look like their mother.
are old.” Individuals should have some understanding of the of what the term ageism but maybe
Wogalter, M. S., & Hosie, J. A. (1991). Effects of cranial and facial hair on perceptions of age and person. The Journal of Social Psychology, 131(4), 589-591. doi:10.1080/00224545.1991.9713892
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
Conversely, this is also a misconception of sorts. Ageism was a concept devised by Butler (1975) to describe how older people in general were discriminated against purely on the basis of being over a certain age by younger members of society (cited in The Open University, 2014c). Using this concept of ageism, Ms Jones is correct in what she is saying, however since Butler and Lewis defined this term, further research has been carried out into ageism and this term has evolved again as society has changed. A more modern take on ageism is defined by Bytheway (2005) cited in the K118 course material (The Open University, 2014d) as “Indeed we are all, throughout our lives, oppressed by ageism, by dominant expectations about age, expectations that dictate how we behave and relate to one another.” In my own personal experience I have been on the receiving end of ageist remarks at different stages in my life. As a teenager, it was perfectly normal for me and my friends to get told off for “loitering” if there was a group of more than 3 of us – 2 teenagers together were tolerated in our town, anymore than that were presumed to be causing trouble, even if we were quite innocently minding our own business. I am now a woman with a 7 year old, and it is amazing how many times I have been asked when my
Czaja, Sara J., and Joeseph Sharit. "The Aging of the Population: Opportunities and Challenges for Human Factors Engineering." National Academy of Engineering. N.p., Spring 2009. Web.
...face reveals: basic and applied studies of spontaneous expression using the facial action coding system (FACS). 2nd ed 2. Oxford: Oxford University Press
SÍTAR, M.E., YANAR, K., AYDIN, S. and ÇAKATAY, U., CURRENT ASPECTS OF AGEING THEORIES AND CLASSIFICATION ACCORDING TO MECHANISMS. .
The lines in your face that appear with age are commonly referred to as frown lines, furrows, and crows ' feet. They are typically caused by years of repeated facial expressions, excess exposure to the sun, cigarette smoking, and other lifestyle
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...
305). Society is constantly bombarded by messages informing us about how to reduce the signs of aging, instead of accepting the natural process (Germov, 2014, p. 305). These beliefs have lead to ageism being very common in society. Ageism is best defined as the negative attitudes, which are associated with the aging process (Novak, 2006, p. 3). Ageism involves an individual or group being stereotyped and experiencing discrimination due to their biological age (Novak, 2006, p. 3). This discrimination can be direct or indirect discrimination, victimisation or harassment (Johnson, 2013, p. 27). Unlike other individuals and groups who are stereotyped and discriminated against, those who are making these comments will one day themselves be of old
[Jain, 2004] Jain, A.K.;Ross, A.;Prabhakar, S.;"An introduction to biometric recognition", Volume: 14 Issue: 1 Issue Date: Jan. 2004, on page(s): 4 - 20
The look that everyone strives for is youthful and attractive. In the movie, The Curious Case of Benjamin Button, Benjamin was born with wrinkles, looking like an eighty-year-old man. Many people did not find him attractive but he could do nothing about it. He knew he was growing up in reverse. As he grew “young”, his wrinkles started to disappear, he could walk better, his gray hair was turning into a more brownish color. Everything you would think about aging, he was going through it in reverse. What was happening to him was the complete opposite of how people usually age. Once people start to hit their middle aged years, signs of aging will visually start to appear such as a few gray hairs, wrinkles. Many people will never forget the day they see their first gray hair or wrinkle (Cavanaugh & Blanchard-Fields, 2015). My sister just turned thirty a few months ago and for most people that is a huge milestone in their lives. Prior to her birthday, she noticed one strand of gray hair, she showed me and I could not see it but she definitely did. It is things like this that make aging the worst thing. Getting gray hair is natural, but today’s society says different. Gray hair is bad, wrinkles are not okay. You do not get Botox? You want to have wrinkles than? There is so much pressure on everyone to stay young and continue to have that youthful look when they
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.