Basics of Face Recognition

When you look at an image of a crowd your brain can immediately figure out who is a familiar face, who is a stranger, who is a man or a woman, who is a child or an adult, and roughly someone’s ethnicity. You can also see the clothing people are wearing, who looks put together and who does not, and what time of day it is or season depending on the foreground and lighting.

A computer can look at the same image and see nothing if we deem it so but with computer vision, it can recognize and identify all the faces, tell you the ages of everyone in the picture, and even accurately tell you everyone’s ethnicity. It may have a harder time determining the season and time of day, due to the shadows, lighting, and shapes, but when it comes to the crowd analytics, verification and recognition it is a breeze.

What is Computer Vision and Pattern Recognition?

Computer Vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.
Pattern Recognition is the automated recognition of patterns and regularities in data.

Step 1 – Pixels

• Pixel is a physical point in a raster image or the smallest addressable element in an all points addressable display device.

• As human eyes see images, a computer only sees in pixels.

• That is how machines are programmed to understand what colours the image pixels are made up.

Step 2 – Image Segmentation

• From there, we can proceed to image segmentation.

• Computers are made to identify a similar group of colours and then segment the image.

• The technique of colour gradient is used to find edges of different objects.

Step 3 – Corner Detection

• Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image.

• A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point.

• An interesting point is a point in an image which has a well-defined position and can be robustly detected.

Step 4 – Image Texture

• An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image.

• Image texture gives us information about the spatial arrangement of colour or intensities in an image or selected region of an image.

• The difference in textures between two objects makes it easier for a machine to correctly categorize an object.

Step 5 – Make a Guess

• After implementing the above steps, a machine needs to make a nearly-right guess and match the image with those present in the database.

• At last, a machine sees the bigger and clear picture and checks if it was right identifying the one, as per the fed algorithmic instructions.

• The accuracy has improved a lot in past years but still, machines make mistakes when asked to handle images with mixed objects.

Why is Computer Vision Important?

We use Computer Vision for face recognition, identification, verification, emotion analysic, crowd analytics and so on.

Computer Vision is also great for:

Object Recognition
Great for retail and fashion to find products in real-time based on an image or scan.

Special Effects
Motion capture and shape capture, any movie with CGI.

Sports
In a game when they draw additional lines on the field.

Social Media
Anything with a story that allows you to wear something on your face.

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