What is Face Recognition with Siamese Network in Computer Vision?
Face recognition systems have emerged as new ways to identify, authenticate, and even authorize human beings. Face recognition techniques aim to detect and recognize human faces in digital media such as images and videos. With better processing power and more research, facial recognition algorithms are becoming more robust, quick, and efficient.
To re-iterate, we use facial recognition techniques when a machine detects a human face and identifies who that human is. In contrast with the same, the phenomenon is termed facial detection when we are figuring out if a face exists in digital media. It is self-explanatory that to perform facial recognition, the first and foremost step is to perform facial detection. The algorithm fixates on the human faces in the digital media and then moves on to attempting to put a label on those faces, which means trying to figure out the identity of the human. This step can prove extremely useful in authentication systems and is more reliable than OTPs and traditional passwords. The author would like to point out that it is very easy to get confused between facial recognition and facial detection; however, if the reader remembers that facial detection serves as the input for facial recognition, it will be easy to draw a contrast between the two.
When dealing with digital media such as images and videos, the first algorithm that comes to our mind is convolutional neural networks (CNN). However, for facial recognition and detection algorithms, an interesting algorithm is deployed, which is known as the Siamese Network. If the readers are familiar with the k-nearest neighbors algorithm, understanding the intuition behind the siamese network would be easy. In the siamese network, no categorization or labeling is performed as opposed to the algorithms the readers have been looking at while working with computer vision. This algorithm focuses on calculating the distance or the dissimilarities between the two images in question. The similarity between two images is inversely proportional to the distance between them.
While working with siamese neural networks, two essential branches play a pivotal role in the working of the algorithm. These branches are called the search branch and model branch.
Here is the general working of a Siamese Network Algorithm:
- The image is first sent through preprocessing techniques.
- The training data serves as an input to the Siamese neural network.
- To obtain better accuracy, the input should be half the width and half the size of the original image.
- The Euclidean distance between the image data points is calculated.
- If this distance is greater than the threshold, the images are said to be the same; if that isn’t the case, the photos are said to be different.
To understand the technicality of what goes under the hood, the Siamese neural network contains two VGG16 layers where the first layer is used for searching (the search branch), and the second layer is used for modeling (the model branch). The image is fed as an input to both these layers, and the distance is then calculated to understand the similarity between both. The importance of utilizing the 2-layer architecture is that it can handle large data sets and multiple facial recognition in one digital media.
While working with image datasets, errors are bound to creep in because of the fundamental drawbacks of computer vision. The images can have different lighting effects, portions of the image can be hidden, and there can also be other parts of the image which are present in a more focal position than the actual face, which has to be detected and recognized. To tackle the same, the image can be divided into high-frequency and low-frequency features and then move on to utilizing our algorithm. This highly enhances the learning power of the Siamese network.
With the availability of better hardware and better processing due to the advancement of cloud-based machine learning, deep learning, and computer vision tools, facial detection and recognition models have started to perform exceptionally well compared to the previous models, which makes them a good candidate for replacing identity cards as well as authentication systems. The major problem that should be addressed is that as humans detect and recognize faces in one instance, our models should also be able to do the same with just one image.
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