Abstract
Gender and age classification is a major role for many purposes in the world. Humans have god-gifted facilities to recognize any gender and their age, but they cannot notice all the people, so the author team has trained the machine to work at those which are not capable of recognizing by simply seeing the people. Nowadays, the age and gender classification have a major role in the market, surveillance, security, etc. This research work for age and gender detection is different from the other project for facial recognition. As in this research, fisherface algorithm has been used which is very easy and accurate, and which simply works on the basis of facial recognition. The authors have used an audience dataset which is today's most demanding dataset as it is a self-updated dataset, and easily available on the open source. It basically depends on the deep learning in which openCv is used for the implementation of the given algorithm and dataset. As it does not require any complex calculations to recognize the faces, it is very fast and easy to use as compared to the other projects.
TopMotivation
The Authors’ Team gets motivated to make this project from the problems in society. Researchers have seen many cases in which people often get confused to recognize the people's gender and people often get confused to predict the age of the people, due to which many problems occur. Today marketing is one of the major factors for the economy, so this project will bring a huge changement in the market. One of the major factors in the market is the demand and the supply.
So, the team doing the study can supply the things in the market if we know the demands of the people according to the gender and the age, so this project will help us to improve the marketing.
The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female.
Age detection using AI typically involves computer vision techniques, which analyze visual data, such as images or video frames, to estimate a person's age. Several methods are commonly employed: Facial Analysis: This is one of the most popular approaches.
Research Objectives
To Design an Age and Gender detector.
- 1.
To guess the age and gender of the human beings.
- 2.
To use fisherface algorithms with openCV.
- 3.
To enable to work with the help of a webcam.
- 4.
To detect face with accuracy.
- 5.
To classify into females and male.
- 6.
To classify into the 8 age ranges.
- 7.
To visualize the analysis of results.
Key Terms in this Chapter
Fisher Face Algorithm: Fisherface is one of the popular algorithms used in face recognition, and is widely believed to be superior to other techniques, such as eigenface because of the effort to maximize the separation between classes in the training process.
OpenCV: (Open Source Computer Vision) is a popular computer vision library started by Intel in 1999. ... It shows you how to perform face recognition with FaceRecognizer in OpenCV (with full source code listings) and gives you an introduction into the algorithms behind.
Argparse: The argparse module makes it easy to write user-friendly command-line interfaces. The program defines what arguments it requires, and argparse will figure out how to parse those out of sys.argv. The argparse module also automatically generates help and usage messages and issues errors when users give the program invalid argum
Python: Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.
Adiencedataset: The Audience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few.