Age Prediction

Introduction

Age is a fundamental characteristic of human beings. Age estimations are based on disparate patterns that emerge from facial appearance. Recently, age estimation has served as a vital attribute in various real-world applications such as these:

• Access control and visual surveillance.
• The human-computer interaction (HCI).
• Marketing intelligence, for instance, the AI product Quividi can enable face and context-responsive advertisement campaigns.

However, the estimation of age from a face image remains challenging since it can be impacted by various factors. These factors include variation in lighting, scale, occlusion, and differences in the age presentation of certain individuals, especially teens and young adults.

Recently, many ML algorithms based on convolutional neural networks (CNNs) models were substantially used for automatic age estimation and classification. These algorithms improved their efficiency and accuracy in this domain. Basically, CNN is a deep learning algorithm that can take an input image and assign importance (learnable weights and biases) to various aspects or objects within this image in order to differentiate one from the other.

Objective

In this lesson, we’ll estimate age using facial images, leveraging the capabilities of the state-of-the-art Python library DeepFace.

The facial age estimation process consists of two major steps:

1. Detecting and localizing faces in an input image.

2. Applying an age prediction model to the localized face that is considered to be the ROIRegion of Interest. The model will then predict the person’s age.

Dependencies

We’ll need the following external Python libraries: