How does a computer learn to recognize a face?

The technology behind face recognition is called computer vision. Computer vision is the science and technology that deals with how computers can be programmed to understand images and video in order to process them in some way. It is a branch of artificial intelligence that focuses on giving machines the ability to see, just as humans do.

The computer vision system used for face recognition is called a convolutional neural network (CNN). Convolutional neural networks are a type of deep learning architecture that has proven very effective at image recognition tasks, such as those related to faces or animals.

Face recognition technology can be used for many purposes like identifying criminals or finding missing persons by Face recognition technology is being used for an increasing number of purposes, but it still has many limitations. One such limitation is the reliance on databases that contain the face of a person who is being searched for. The bigger the database, the better the accuracy will be when attempting to identify someone.

There are two categories of face recognition technologies:

  • One category is based on the geometry of a human face, such as the distance between eyes, nose, and mouth.

The field of facial geometry is a relatively new branch of mathematics that has so far not been utilized much outside the fields of biology, anthropology, and zoology. However, in recent years, mathematicians and computer scientists have been working to develop algorithms and software to measure facial geometry from photographs or 3D models. For example, researchers at MIT are developing artificial intelligence tools for analyzing facial images to determine gender, emotional states, age, and ethnicity in order to increase the accuracy of their detection and improve the user experience for different user types by understanding someone’s race, ethnicity, nationality or country of origin.

  • The second category is based on the texture of a human face, such as the contours of each individual’s unique skin pores

The second category is based on the texture of a human face, such as the contours of each individual’s unique skin pores, or the direction and density of an individual’s hair follicles. This data can be used to analyze an individual’s age and gender.

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  • Naveen Pandey Data Scientist Machine Learning Engineer

    Naveen Pandey has more than 2 years of experience in data science and machine learning. He is an experienced Machine Learning Engineer with a strong background in data analysis, natural language processing, and machine learning. Holding a Bachelor of Science in Information Technology from Sikkim Manipal University, he excels in leveraging cutting-edge technologies such as Large Language Models (LLMs), TensorFlow, PyTorch, and Hugging Face to develop innovative solutions.

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