New Gender/Age Classification Model Deployed

Here at PixLab, we recently deployed on production, a brand new gender/age classification model available to our customers via the FACEMOTION API endpoint.

gender age detection

  • The new model implementation is based on the ResNet-50 convolutional neural network (CNN) that is 50 layers deep. The network can easily classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

  • The reference, implementation paper is from: Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019 (

  • The Python/PHP code samples listed below should be able to easily output the age estimation, gender, and emotion pattern by just looking at the facial shape of any present human face in a given picture or video frame using our new classifcation model.

Python Code

  • FACEMOTION is the sole endpoint needed to perform such a task. It should output the rectangle coordinates for each detected human face that you can pass verbatim if desired to other processing endpoints like CROP or MOGRIFY plus the age estimation, gender and emotion pattern of the target face based on its facial shape.
  • Finally, all of our production ready, code samples are available to consult at our samples page or the PixLab Gihtub repository.

Automatically Filter Image Uploads According to their NSFW Score

Our colleague Vincent just published an interesting blog post on on how to automatically filter images uploads (GIF included) according to their NSFW score via the PixLab NSFW API endpoint and apply a blur filter if adult, nudity or gory details is detected. Find out more information via the following links:

SOD CV/ML Library 1.1.8 Released

The PixLab development team is pleased to announce the immediate availability of the 1.1.8 release of our Embedded Computer Vision & Machine Learning library SOD.

SOD Face detection

SOD is an embedded, modern, cross-platform, computer vision and machine learning C/C++ library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. At PixLab, we believe SOD is:

  • Suitable for deep learning on limited computational resource, embedded systems and IoT devices.
  • Easy to integrate with existing code bases. Interpolatable with OpenCV and/or any other proprietary API.

SOD is shipped with a real-time face detection & tracking model (download link) that has been ported to Unity, Unreal Engine and WebAssembly.

Finally, you can find out more information about the SOD project via the following links: