Data-Science Series(Practical-11)-predict the gender and age range of an individual in Python
In this article, we will test the data science model using our own image to predict the gender and age range.
Age and gender, two key facial attributes, play an important role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications such as access control, human-computer interaction, law enforcement, marketing intelligence, and visual surveillance, among others.
In this case, we did Gender Detection, which is predicting ‘Male’ or ‘Female’ utilizing deep learning libraries and OpenCV to mention the projected gender. Age detection is the method of automatically determining a person’s age based merely on a snapshot of their face. There are several age detector algorithms, however, deep learning-based age detectors are the most common.
Typically, you’ll see age detection implemented as a two-stage process:
1. Detect faces from an input image
2. Extract the face Region of Interest (ROI), and apply the age detector algorithm to predict the age of the person
In stage 1, any face detector capable of producing bounding boxes for faces in an image can be used. The face detector produces the bounding box coordinates of the face in the image.
In stage 2, identifying the age of the person.
Given the bounding box (x, y)-coordinates of the face, we first extract the face ROI, ignoring the rest of the image/frame. Doing so allows the age detector to focus solely on the person’s face and not any other irrelevant “noise” in the image.
The face ROI is then passed through the model, yielding the actual age prediction.
The steps we must take are as follows.
Step 1: Importing libraries
Step 2: Finding bounding box coordinates
Step 3: Loading model and weight files
Step 4: Mentioning age and gender category list
Step 5: Function to predict gender and age
Final result:
GitHub link:
