In any given episode, the security department at the fictional Montecito Hotel and Casino uses its video surveillance system to pull an image of a card counter, thief or blacklisted individual. It then runs that image through the database to find a match and identify the person. By the end of the hour, all bad guys are escorted from the casino or thrown in jail.
And Baidu is using face recognition instead of ID cards to allow their employees to enter their offices. These applications may seem like magic to a lot of people.
But in this article we aim to demystify the subject by teaching you how to make your own simplified version of a face recognition system in Python.
Github link for those who do not like reading and only want the code Background Before we get into the details of the implementation I want to discuss the details of FaceNet. Which is the network we will be using in our system.
FaceNet FaceNet is a neural network that learns a mapping from face images to a compact Euclidean space where distances correspond to a measure of face similarity.
That is to say, the more similar two face images are the lesser the distance between them. Triplet Loss minimises the distance between an anchor and a positive, images that contain same identity, and maximises the distance between the anchor and a negative, images that contain different identities.
An example of a Siamese network that uses images of faces as input and outputs a number encoding of the image.
Coursera FaceNet is a Siamese Network. A Siamese Network is a type of neural network architecture that learns how to differentiate between two inputs. This allows them to learn which images are similar and which are not.
These images could be contain faces. Siamese networks consist of two identical neural networks, each with the same exact weights. First, each network take one of the two input images as input.
Then, the outputs of the last layers of each network are sent to a function that determines whether the images contain the same identity. In FaceNet, this is done by calculating the distance between the two outputs. Implementation Now that we have clarified the theory, we can jump straight into the implementation.ZoOm observes the user’s head, neck, ears, hair, facial features and their environment as the camera is moved closer to the face.
During the motion, the camera’s view of the face changes and perspective distortion will be observed if the face is 3D. Face Recognition System Matlab source code for face recognition. EigenFaces-based algorithm for face verification and recognition with a training stage.
Matlab/5(4). A MATLAB based Face Recognition System using Image Processing and Neural Networks Jawad Nagi, Syed Khaleel Ahmed Farrukh Nagi. A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. The facial recognition software runs silently in your system, collecting data on each face that it detects; this data is then stored within an easily accessible database.
A user will then be able to access this database, and will be given the option to select a particular face. Face recognition is neither new nor rare. FBI face recognition searches are more common than federal court-ordered wiretaps.
At least one out of four state or local police departments has the option to run face recognition searches through their or another agency’s system.