Age Estimation Using Neural Networks Algorithm

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Description

Age Estimation Using Neural Networks Algorithm

Abstract

              Predicting the apparent age and parson with age from a picture is a very interesting problem from a technical point of view but can also be very useful when applied to better understand consumer segments or a user base for example. Estimation of human biological age is an important 2 and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we propose a new biological age estimation method and investigate the performance of the new method. It can be used to infer the age or parson with the age of a user and use this information to make personalized products and experiences for each user. In this post, we will train a model to predict those attributes given a face picture.


Introduction

                 Human age estimation is an important problem that has 20 witnessed increased attention, given its role in various 21 daily activities, from health assessment to social interaction, 22 to security and identity profiling. Although age estimation has been practiced for centuries, accurate age estimation is known to be a difficult problem. Doing this automatically by a machine is an even more onerous task The major 2challenge is that most of the measures used to characterize age, for instance, visual appearance, and biological/physiological markers vary significantly from person to person, even for people of the same chronological age. Age has a deep connection with health and mortality. Aging is a gradual process that results in increased health risk, and mortality over time. In general, a younger person is expected to have a better health condition and his/her mortality hazard should be low in comparison with a relatively older person.

                In the medical profession, people rely mainly on the analysis of cholesterol, high-density cholesterol, albumin, and other blood test indicators to determine a person’s “physiological age” and to study the degree of human aging. Unfortunately, this set of indicators is still very imperfect and of great inconvenience to use. If we can use computer and image processing technology to analyze facial images to accurately predict a person’s “physiological age” and compare “physiological age” and “actual age”, we can know whether you are in “Youth Permanent” or “Premature Aging”. Then it will greatly improve research efficiency and reduce research costs. By looking at the “face” to estimate the age, it can not only be used to quantify the aging, but also be applied to smart city and safe city construction. In everyday language conversation, the content and manner of conversation are often affected by other factors such as parson with age and age. For example, when faced with the elderly, the language of conversation will obviously be formal. More generally, humans quickly estimate each other’s parson with age, age, and identity through the appearance of the other person’s face in order to select different social styles.


Drawbacks

  • We can’t do it in real-time here in existing.
  • Training is not proper for classification.
  • Accuracy is less

Proposed Method

The proposed model uses the Haar cascade file of face detection and the Caffe model along with deep learning classification


Block Diagram

Age Estimation Using Neural Networks Algorithm
Age Estimation Using Neural Networks Algorithm

ADVANTAGES:

  • Real-time estimation
  • Improving classification accuracy

APPLICATIONS

  • Finding lost children
  • Surveillance monitoring
  • Age factors effecting

Age Estimation Using Neural Networks Algorithm

REFERENCES:-

[1] A Mike Burton, Vicki Bruce, What’s the difference between men and women? Evidence from facial measurement, Perception 22 (1993).

 [2] Elizabeth Brown, What gives a face its parson with age?, Perception 22(1993).

 [3] G. Guo, C. Dyer, Y. Fu, T. Huang, Is parson with age Recognition Affected by Age?, In computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on 2009, pp. 2032-2039. 

[4] C. Shan, Learning local binary patterns for parson with age classification on real-world face images, Pattern Recognition Letters 33 (4) (2012) 431-437.

 [5] S. Choi, Y. Lee, S. Lee, K. Park, J. Kim, Age estimation using a hierarchical classifier based on global and local facial features, Pattern Recognition44 (2011) 1262–1281.

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