i try haar_cascade for face detection and LBPH for face recognition , but the result wasn't good enough, please suggest good ways to detect and recognize faces.
my aim is to create an app which take a photograph of students and by scanning this one photograph, it will predict which student is present and which is absent
You can use the Facial landmarks library, it's very easy to use you will get 68 point and compare them from your dataSet very dirty and easy way of doing things note that
One can use facetorch - Python library for face analysis using neural networks written in PyTorch. It includes:
Face Detection State-of-the-art practical considerations
Face Detection is often the first stage of a Computer Vision pipeline. Thus, it is important for the algorithm to perform in real time. So, it is important to know the comparison between various face detection algorithms and their pros and cons to use the right algorithm for your application. There are many algorithms that have been developed over the years as shown below.
Our recent favorite is YuNet because of its balance between speed and accuracy. Apart from that, RetinaFace is also very accurate but it is a larger model and is a little slow. We have compared the top 9 algorithms for Face Detection on some of the features that we should keep in mind while choosing a Face Detection algorithm:
Speed
Accuracy
Size of face
Robustness to occlusion
Robustness to Lighting variation
Robustness to Orientation or Pose
You can check out the Face Detection ultimate guide that gives a brief overview of the popular face detection algorithms.
Did you look into a (pre)trained convolutional net? I have not used any pre-trained facial model (trained other objects with tensorflow though) but I'm pretty sure a quick search will yield quite a few semi off the shelf models.
That should be pretty much the state of the art, quite effective even with simpler models, but also widely available pretrained (specially for more widely used objects such as faces)
