The algorithms need to be trained on how to localize faces, extract features from them and then identify them. Deep learning systems use existing databases of millions of images to train face recognition systems using detection, extraction and comparing algorithms.  

These algorithms are designed like an animal’s neural network and are called Artificial Neural Networks (ANN). Two types of ANNs – convolutional neural networks and deep auto-encoder networks – are the most popular algorithms for training facial recognition systems. 

Convolutional neural networks 

It is a set of deep feed-forward artificial neural network to analyze visual images. CNN can take into account the 2D topology of an image, and minimizes the effect of scale changes, turns and angles, biases and other distortions in the input image. 

It is used in  

  • Supervised learning for object classification, recognition and detection 
  • Unsupervised learning for image segmentation 
  • Image Compression 

Deep auto-encoder networks 

These are networks used in an unsupervised learning mode for reducing the dimensionality of the input. It helps in optimizing the time required for matching the given input with that in the database. Basically, an auto-encoder is a set of encoder and decoder. The encoder takes the input, shrinks it into a simple vector which then passes through the CNN. The output provided by the CNN is then decoded and provided as output. Since the convoluted neural network gets a compressed feature vector, the time and resources required for identifying the face is optimized. 

Face recognition frameworks 

Facial recognition is one the features of many apps being developed. Therefore, developers cannot be expected to start facial recognition from scratch, writing their own algorithms and training them using huge data sets. Access to such high volumes of images itself would be a challenge. Also, most of the algorithms are specialized, doing just 2-3 of the steps really well. To build a facial recognition system, developers need to use at least two algorithms to achieve the desired outcome.  

There are many facial recognition frameworks and APIs available, which can be used to build the facial recognition systems. Some of the most popular ones in 2020 include: 

  • Microsoft computer vision 
  • Inferdo 
  • Kairos 
  • Face++ 
  • EyeRecognize 
  • Lambda Labs 
  • OpenCV 
  • Animetrics 

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