Siamese Neural Network: Face Recognition Technology

When it comes to properly recognizing new patterns, humans are really good. People appear to be able to swiftly grasp new concepts and then distinguish variations on these concepts in subsequent perceptions. This is especially evident when stimuli are provided to anything and everything. A clear demonstration of a person’s intelligence and what true intelligence looks like is the ability to recall a face with only one glance. Face recognition is one of the most useful advances in technology: learn how this asset works, through siamese neural network.

Siamese Neural Network: Face Recognition Technology

Machine learning was incredibly useful and successful in a variety of applications, including web search, fraud detection, text generation, and speech and image recognition, while attempting to acquire human-like capabilities. But when put under pressure to make predictions about data with little supervised knowledge, these algorithms frequently fall short. Image categorization with a size limit on the data is one job that is very intriguing. For example, before making a forecast, we might only have one sample of each potential class. One-shot learning is what we call this. when the learning algorithm examines an image only once and picks out traits to set it apart from others. With one-shot learning, a computer can tell from a single glance whether Tony stark is in a picture.

In situations involving facial recognition, one-shot learning can be used successfully. Typically, face recognition has found use in a variety of sectors, including supply chains, retail, surveillance, social media, and hospitality. Several of the well-known use cases include:

Face recognition is frequently used to quickly detect people entering retail outlets who are known shoplifters, organized retail criminals, or people with a history of fraud.

Finding a missing person: By allowing operators to add a photo of a missing person and compare it to previous occurrences of that face captured on CCTV system, facial recognition techniques like one-shot learning in CCTV systems can significantly enhance operators’ efforts.

To protect law enforcement, CCTV surveillance systems can be equipped with face recognition technology. Police can use it to locate and identify alleged criminals from the past.

Identifying users on social media and emotion detection – Recognizing and mapping a person’s facial expressions could be another helpful social media tool.

You might be reading this blog on a face recognition-enabled gadget or holding a piece of facial recognition software in your hand.

You may also like: Chatbots with Artificial Intelligence increases consumer interaction

Siamese Neural Network

Facial recognition is one of the crucial uses for one-shot learning in Siamese neural networks. Let’s examine how one-shot learning permits facial classification and recognition.

Siamese networks, a particular kind of neural network, were able to practically achieve one-shot learning because they learn the similarity between two points before differentiating them rather than learning to classify their inputs. Imagine telling a friend, “You look far better than him/her,” in a complement. The Siamese neural network considers how much better-looking you are than the average person.

Simply described, a Siamese network consists of two sister networks—similar or identical neural networks—each of which receives one of the two input images, for face recognition. The contrastive loss function, which determines the similarity/distance between the two images, is then fed the final layers of the two sister networks.

The Siamese neural network’s main goal is to distinguish between input images rather than classify them. The effectiveness of the sister networks in separating a specific set of images is assessed using a contrastive loss function.

An image serves as the first input to the sister network, which is then followed by a series of feature extraction layers (convolution, pooling, fully connected layers), from which we eventually obtain a feature vector f. (x1). The input is encoded in the vector f(x1) (x1). The second operation is then carried out by feeding the input to the second sister network, which is the same network as the first, in order to obtain a new encoding of the input, f(x2) (x2).

The distance d between the encodings f(x1) and f is then determined (x2). The two photos are of the same person if the distance d is less than a predetermined threshold; else, they are of distinct people. This is how a computer distinguishes between faces so that it can recognize them.

The distance between two encodings:

Euclidean Distance (f(x1), f(x2)) = d(x1,x2)

D(x(i),x(j)) is tiny if x(i), x(j) are the same person.

D(x(i),x(j)) is high if x(i), x(j) are different people.

By using gradient descent on a triplet loss function, sometimes referred to as contrastive loss, we can train the parameters to produce a good encoding for the input image. In other words, we will use a positive, anchor, and a negative picture to calculate a loss function. Here, the positive image serves as the anchor image and is identical to the negative image.

The distance d(A, P) between the encodings of the positive and anchor images will be smaller than or equal to d(A, N) since the encodings of the negative image and anchor are the same.

d(f(A),f(P)) ought to be minimal.

d(f(A),f(N)) need to be substantial.

“d(f(A), f(P)” becomes “d(f(A), f(N)”

Loss function

max(|| f(A) – f(P) ||2 – || f(A) – f(N) ||2 + alpha, 0) is the formula for L(A,P,N).

The maxima here suggest that the triplet loss function L(A, P, N) will be zero as long as [d(A, P) – d(A, N) + alpha] is less than or equal to zero, but if it is more than zero, the loss will be positive and the function will attempt to minimize it to zero.

The cost function(J), which represents the total of all individual losses derived from various triplets across the entire training set, is then calculated.

L(A(i), P(i), N(i)) = J

Siamese neural network face recognition implementation

Description:

We evaluated the Siamese neural network and how well it recognized various photographs of people. The Siamese Neural Network had been put into use in two separate methods.

Siamese Neural Network for Facial Recognition

We created a Face Verification system that allows access to the list of residents and employees. For instance, each person entering an office must identify themselves at the entry by swiping their ID card (identity card). Then, the face verification system verifies that the person is who they say they are.

Siamese Neural Network for Face Identification

We have also put in place a facial recognition technology that uses a picture as input to determine if the person is one of the allowed individuals (and if so, who). We will no longer receive a person’s name as one of the inputs, in contrast to the prior face verification method.

Conclusion

A cutting-edge method that is rapid and effective in finding differences between photos is the siamese neural network: the ability of algorithms to swiftly pick up on essential aspects and distinguish between them creates new application possibilities, particularly in the areas of video surveillance, face recognition, self-learning tasks, etc. We discovered that the Siamese neural network is computationally very expensive and time-consuming, requiring both time and hardware resources to train the model. It also has the fundamental flaw of trying to fit each face into one of the predetermined identities or images. The computer will give a fresh face on the screen one of the two identities. By carefully selecting a threshold value, this issue can be solved and the similarities can be seen more clearly.

Siamese Networks | Face Recognition | Computer Vision on Humans

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