Human Age Estimation from Facial Images Using Artificial Neural Network

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

A variety of factors contribute to the signs of aging on the face. These factors include a decrease in the production of collagen, the constant pull of gravity, stress, genetics and various environmental factors (including exposure to UV light).

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.

There exist two famous public domain datasets for aging faces: FGNET and MORPH. The FGNET dataset is a relatively small dataset. It consists of 1002 face images of 82 different persons. The MORPH database was collected over a span of 5 years with numerous images of the same subject. This is not a controlled collection (i.e., it was collected in real-world conditions). MORPH dataset has two versions: Album 1 and Album 2. The MORPH Album 1 contains 1690 face images from 625 different persons. MORPH Album 2 is a database of mugshot images, with associated metadata giving the age, ethnicity, and gender of each subject in the database. Album 2 contain 78, 000 images from 20, 000 different people with each person having at least two cross-age face

In this survey, mainly exposes the different types of face annotation techniques. Face annotation is an exigent problem in the field of image analysis and computer vision has established many applications in different domains. Face annotation (or “face image tagging”) is becoming growingly popular both on the web and many organizations. A face annotation is defined as the image providing information about the facial images. Face annotation system is trying to attach a label to each and every face images in the database. The most important use of face annotation is to manage and organize the huge number of facial images collections in several organizations.

In addition, unique identification techniques such as facial aging and facial marks assist facial recognition to cover bases that DNA and fingerprint are unable to. The recognition tool can identify people even with facial aging. If a 10-year-old child

Use combined comparisons of image views, graphs, numerical data with zooming and panning capabilities, 3D skin surface viewing, and age simulation to

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

The ratio of the morphological facial height to facial width and multiplied by 100 is known as facial index and the total facial index exhibits sexual differences and different shapes of face. (K Sharma et all, 2014). There are several factors that affect the facial index of an individuals including race, gender, age, ethnic, climate, nutritional, socio-economic as well as genetic factors (D.Jeramic et all, 2013). Based on facial index, we can determine the types of faces that are classified into following five groups according to Banister’s classification which are hypereuriproscopic (very broad face), euriproscopic (broad face), mesoproscopic (round face), leptoproscopic (long face), hyperleptoproscopic (very long face).

Abstract— There is an urgent need to organize and manage images of people automatically due to the recent explosion of such data on the Web in general and in social media in particular. Beyond face detection and face recognition, which have been extensively studied over the past decade, perhaps the most interesting aspect related to human-centered images is the relationship of people in the image. In this work, we focus on a novel solution to the latter problem, in particular the kin relationships. To this end, we constructed two databases: the first one named UB KinFace Ver2.0, which consists of images of children, their young parents and old parents, and the second one named FamilyFace. Next, we develop a transfer subspace learning based algorithm in order to reduce the significant differences in the appearance distributions between children and old parent’s facial images. Moreover, by exploring the semantic relevance of the associated metadata, we propose an algorithm to predict the most likely kin relationships embedded in an image

This paper presents novel approach to classifying gender from frontal facial images. Gender classification is one of the focuses of Human Computer Interaction (HCI) problem and has many potential applications. When we communicate with other people we process information about the person, such as the expression, gender, ethnicity and age. We hope human machine communication could flow more freely if the computer can comprehend

Gerontology is a multidisciplinary study. We cannot study aging based on a few simple factors. There are many different variables that go into how a person ages. Everyone comes from a different background, different genetics, and goes through different life experiences. This is why no one person can be aged using the same method. The most common way people measure age is through chronological age. Chronological age is simply counting the amount of literal years a person has been alive for. This method of measuring aging is not effective in comparing peoples’ ages. More effective processes to measure aging are “biological (functional capacity), psychological (sensory, mental, personality), and social age (society’s roles and expectations)” [1]. By combining these methods, we can more accurately study the effects of aging on people.

very sensitive area. Age can be inficated even by the color of hair and clothing, bright for young,

The below fig.4 shows the Excepted result of the perfeormance of calculating precision and recall for better annotaion of faces into videos improvement of processing time.It will help to search engine for faster search of videos.