1.1 Introduction
The human face holds very important quantity of attributes and information about the person, such as expression, ethnic, gender, and age. Human beings can detect and analyze these information easily, for instance, the majority of people are able to recognize human traits like gender, where they can tell if the person is male of female by only seeing his/her face. Likewise, they can determine the age of the person and say whether this person is a child or an adult. On the other hand, constructing applications to identify the people from their face and extract their age and gender information is a challenging task for computer vision, which modern world going to depend on it in many important sides of our daily life, because of the necessity of creating a general model that works for all human subjects.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Research hypothesis and questions, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
Age and gender prediction systems have been growing rapidly in recent days due its important modules and beneficial uses for many computer vision applications such as human-computer interaction, security systems, and visual surveillance. There are many examples that demonstrate the importance of a gender and age prediction. For instance, there are a specific age for getting alcohol, driving vehicles, traveling alone abroad, smoking cigarettes, etc. But the problem is that human skills of age prediction are limited and not accurate. Therefore, computer vision systems would be helpful to deny under-aged people. Another example is that following the increase of terrorist threats, airports are considering security measures at the security checkpoints to collect the gender information of the passengers automatically, which may help in observing a certain segment of people.
Automatic age and gender prediction systems are currently being used by hotels, airports, bus stations, casinos, government buildings, universities, hospitals, cinemas, etc. to increase the level of security and facing any possible threats or deficiencies. Besides the security applications, age and gender prediction techniques are used also in health care systems, information retrieval, academic studies and researches, and Electronic Customer Relationship Management (ECRM) systems, where customers are distributed to different gender and age groups like children, teenagers, adults and senior adults in addition to determine whether they male or female.
Furthermore, gathering some customer's daily life information like activities, habits, traditions, priorities etc. may help the corporations to classify products and services depending on their gender or age groups, which lead to increase their incomes and earn more money. For example, clothes stores may offer appropriate fashions for males or females according to their age groups; restaurants want to know the most popular meals for each age or gender group; many companies want to make specific advertising to specific audiences depending on their gender or age groups.
It is challenging to determine an accurate age from a single shot due to factors such as cosmetics, environmental lights, impediments, and facial expressions. As a result, instead of treating this as a regression problem, we treat it as a classification challenge. Therefore, in Nigeria where the research was carried out, the activities that was conducted is to analyze the gender detection and age prediction.
1.3 Statement of Problems
Investigation reviewed that gender classification, and age estimation from facial images. Thus, our main challenge is to propose a methodology, design, and implementation of accurate gender classification and age estimation systems, which can perform and obtain high accuracy by using and combining several feature extractors. There are many applications and studies focusing on predict the age and gender of individuals from facial images. However, many obstacles may lead to incorrect results.
1.4 Aim and Objectives of Study
The aim of the study is to analyze the gender detection and age prediction. In achieving this aim, the following specific objectives were laid out as follows:
- To determine whether the age of an individual can be predicted from their facial images
- To find out if the gender of an individual can be detected from their facial images
- To increase the performance and accuracy of predicting gender and age of people from their facial images.
1.5 Research Questions
The study came up with research questions so as to be able to ascertain the above stated objectives. The specific research questions for the study are stated below as follows:
- Can the age of an individual be predicted using their facial images?
- Can the gender of an individual be detected using their facial images?
- What is the level of performance and accuracy of predicting gender and age of people from their facial images?
1.6 Research Hypothesis
In order to pursue the objective of this study, the following generalized statements have been designed to guide and aids in obtaining the result for the experiment to be conducted. For this work, the null hypothesis will be represented with H0 while the alternative hypothesis will be represented with hypothesis H1.
Hypothesis One
- H0: The age and gender of an individual cannot be predicted using their facial images
- H1: The age and gender of an individual can be predicted using their facial images
1.7 Significance of Study
This study will be of immense benefit to scientist and other researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.
1.8 Scope of Study
The scope of the research is focused on Analysis of Gender Detection and Age Prediction using their facial images.
1.9 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
