The age and gender recognition library can be integrated into software that needs accurate estimation of audience demographics from facial images. It is robust under different lighting conditions and different angles. It can operate on embedded hardware, on-premise servers or can be deployed as cloud API.
There are lots of industries that could benefit from our face recognition module, including security, marketing and advertising. Typical applications, to name a few are:
The age and gender classifier is a neural network model in TensorFlow format. It is is based on the fast MobileNet neural network architecture. It runs in real time on CPU of a regular PC while at the same time achieves state-of-art accuracy. One facial image classification takes 35 milliseconds on Intel i5 CPU. For faster inference a NVIDIA GPU is recommended to be used. There are many ways to integrate the age and gender classifier into your software. For example, the model can be opened in OpenCV by DNN module. It is also possible to use TensorFlow library and to run the classifier using C++ or Python. Another option is to use TensorFlow Serving, which is a high-performance serving system for machine learning models, designed for production environments. It exposes RESTful API (in port 8501) and gRPC interface (in port 8500). The model server can be packaged in Docker container and to be hosted on the cloud or On-Premises servers.
Company | Gender prediction accuracy (%) | Age estimation mean absolute error (in years) |
---|---|---|
Spectrico | 98.3% | 5.5 |
Microsoft | 96.9% | 8.5 |
Skybiometry | 92.9% | 7.2 |
Sighthound | 91.1% | 8.1 |
Eyedea | 90.7% | 8.0 |
VisageCloud | 91.1% | 9.1 |
Public safety and security organizations can include advanced search and human analytics functionalities into their software to find or redact relevant information in video records.
Marketing and retail specialists can use our age and gender estimation to target their ads, content, products, or shelf placement towards a specific audience.
Organizing, storing and retrieving of multimedia content like photos and videos. Building searchable face image databases for video and images archives.