KBY-AI released the accurate age and gender detection SDKs by leveraging deep learning techniques to accurately identify the gender and age of a person from a single image of a face. Age and gender classification has become increasingly relevant to a wide range of applications, especially with the rise of social platforms and social media.
Unlike many other computer vision applications, age and gender detection is an inherently difficult problem.
The type of data required to train these systems is the main reason for the difference in difficulty. Compared to datasets with age and gender labels—which typically contain only thousands or, at best, tens of thousands of images—general object classification tasks often benefit from datasets with hundreds of thousands or even millions of training images.
KBY-AI achieved high accuracy in age and gender detection by leveraging deep learning techniques and has released mobile(Android, iOS) and web server(Linux, Windows) SDKs for customers.
Age and Gender Detection Terms
In this section, we introduce key technical terms related to the development of age and gender detection model to aid understanding.
What is Computer Vision
Computer Vision is the field of study that enables computers to see and identify digital images and videos as a human would. The challenges it faces largely follow from the limited understanding of biological vision. Computer Vision involves acquiring, processing, analyzing, and understanding digital images to extract high-dimensional data from the real world in order to generate symbolic or numerical information which can then be used to make decisions. The process often includes practices like object recognition, video tracking, motion estimation, and image restoration.
What is OpenCV
OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks TensorFlow, Caffe, and PyTorch.
What is CNN
A Convolutional Neural Network is a deep neural network (DNN) widely used for the purposes of image recognition and processing and NLP. Also known as a ConvNet, a CNN has input and output layers, and multiple hidden layers, many of which are convolutional. In a way, CNNs are regularized multilayer perceptrons.
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that take an image as input and learn its features through the use of filters. This enables them to identify important objects within the image, allowing them to distinguish one image from another.
For example, a convolutional neural network can learn the distinct features of cats that differentiate them from dogs. As a result, when given images of cats and dogs, it can accurately distinguish between the two. One key advantage of convolutional neural networks over traditional machine learning algorithms is their ability to perform automatic feature extraction, eliminating the need for manual data pre-processing.
As a result, CNNs reduce the need for extensive data pre-processing. While the filters may initially require some manual tuning during the cold start, they gradually adapt and learn relevant features as training progresses. This allows CNNs to evolve continuously as more data becomes available.
What is Deep Learning
Deep learning is a branch of artificial intelligence and a subset of machine learning that involves networks capable of learning from unlabeled or unstructured data. It is a powerful technique that can be used for tasks such as age and gender detection from image files.
Deep learning algorithms are primarily used to enhance computers’ ability to perform tasks typically requiring human intelligence. The term ‘deep learning’ doesn’t imply deeper understanding, but rather refers to the use of multiple layers of representations within neural networks.
The term ‘depth’ in a deep learning model refers to the number of layers it contains. This concept could also be described as learning multi-layer representations or learning complex classification tasks. Modern deep learning techniques often include dozens, and sometimes even hundreds, of representation layers.
These layers are actively trained using labeled data. During training, the network learns useful representations for tasks such as digit classification in its initial passes. Structural changes to the network can also be made to encourage the development of desired representations at different layers.
Age and Gender Detection Project Overview
Age and gender play fundamental roles in social interactions. Different salutations and grammar structures are held for men and women in different languages, and different dialects are frequently employed when addressing older compared to young people. Despite the importance of these attributes in our daily lives, the ability to estimate them efficiently from face images is still far from meeting the requirements of commercial applications.
Age and gender detection have been around for a time, but improvements are still being developed. Since the creation of social media platforms, this has been the situation. With the rise of artificial intelligence came an increase in performance and the capacity to train a model to achieve age and gender detection, visible intelligence has become more important in the computer vision industry.
Objective
We are focusing on building age and gender detection model that can approximately guess the gender and age of the person (face) in a picture using Deep Learning on mobile or web server.
Project Configuration
In this Project, we would use Deep Learning to accurately identify the gender and age of a person from a single image of a face. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, we make this a classification problem instead of making it one of regression.
Dataset
The UTK Dataset comprises age, gender, images, and pixels in .csv format. Age and gender detection according to images has been researched for a long time. Over the years, different methodologies have been used to handle this issue. Presently, we start with the assignment of recognizing age and gender utilizing the Python programming language.
Keras is the interface for the TensorFlow library. Use Keras if you need a profound learning library that allows simple and quick prototyping (through ease of use, seclusion, and extensibility). It supports convolutional networks and repetitive organizations, as well as blends of the two. It runs flawlessly on CPU and GPU.
Fundamentals of Image Processing
The upgrading of image pictures taken from camera sources, from satellites, aeroplanes, and the images caught in everyday life is called picture processing. Processing the image based on analysis requires many different techniques and calculations. Digital-formed pictures need to be carefully imagined and studied.
Image processing has two main steps followed by simple steps. To improve an image and produce more high-quality pictures, other programs can be adopted, such as picture upgrades. The other procedure is the most pursued strategy for extracting data from a picture. The division of images into certain parts is called segmentation.
The location of the information accessible in the pictures is much-needed information. The image’s data is to be changed and adjusted for discovery purposes.
Just as the issue is expunged, different procedures are required. In a Facial identification strategy, the articulations that the faces contain hold a great deal of data. At whatever point the individual associates with the other individual, many ideas are associated.
The evolving of ideas helps in figuring out certain boundaries. Age assessment is a multi-class issue in which the years are categorized into classes. Individuals of various ages have various facial features, so it is hard to assemble the pictures.
To identify the age and gender of several faces’ procedures are followed by several methods. From the neural network, features are taken by the convolution network. The image is processed into one of the age classes in light of the prepared models. The highlights are handled further and shipped off the preparation frameworks.
KBY-AI’s Age and Gender Detection Mobile SDK
KBY-AI is a leading provider of identity verification solutions and is continually releasing new products by leveraging state-of-the-art (SOTA) techniques. KBY-AI’s face SDK includes 3 kinds of packages like Basic, Standard, and Premium. Here, Premium package just includes age and gender detection functionality for both Android and iOS platforms.
| Basic | Standard | Premium |
|---|---|---|
| Face Detection | Face Detection | Face Detection |
| Face Liveness Detection | Face Liveness Detection | Face Liveness Detection |
| Pose Estimation | Pose Estimation | Pose Estimation |
| Face Recognition | Face Recognition | |
| 68 points Face Landmark Detection | ||
| Face Quality Calculation | ||
| Face Occlusion Detection | ||
| Eye Closure Detection | ||
| Age, Gender Estimation |
Age and gender detection mobile SDK was built on multiple platform and multiple programming languages by including Flutter, Native Android, iOS, React-Native and Ionic Cordova, etc.
They have already pushed demo application for age and gender detection to Google Play so that anyone can test it easily by installing it on Android device from Google Play.
If you want to integrate KBY-AI’s face SDK into your project, you can contact them to request a free trial license to evaluate their products through Email, Whatsapp, Telegram or Discord. They are providing 24/7 technical support via messenger.
Frequently Asked Questions
What is age and gender detection?
Age and gender detection is the process of automatically identifying a person’s age group and gender using technology, typically through images, videos, or real-time camera feeds. It’s a part of computer vision and AI that uses deep learning models to analyze facial features.
Where can we get the powerful age and gender detection SDK?
KBY-AI provides customers with age and gender detection SDK as a premium package for Android, iOS and web server.
Does KBY-AI SDKs supoprt cross compile for multi-platform?
Yes, every their SDK includes mobile version(Android, iOS, Flutter, React-Native, Ionic Cordova), C# version and server version.
How can I know the price detail for KBY-AI SDKs?
You can contact them through Email, Whatsapp, Telegram or Discord, etc through Contact Us page below.
Is the image or data stored?
No, KBY-AI age and gender detection SDK works fully offine and on-premises solution.
Conclusion
Age and gender detection is a powerful AI-driven technology that enables systems to automatically estimate a person’s demographic profile based on facial features. By leveraging deep learning and computer vision, it plays a crucial role in applications ranging from personalized services and retail analytics to security and human-computer interaction.
As this technology continues to evolve, it offers increasing accuracy and real-time performance, making it a valuable tool in modern intelligent systems. However, it’s essential to ensure ethical use and compliance with privacy regulations when deploying such solutions.
