All You Need To Know About Facial Recognition Systems

Have you ever wondered how facial recognition systems work? Ever wondered how you recognize faces? The mechanisms behind both natural and artificial facial recognition are incredibly complex.

Humans have a dedicated area of the brain that’s solely devoted to the task, the fusiform gyrus. According to an article at Science.org, the neurons in this area helps us recognize faces with impunity. When designing an artificial face recognition system, developing a formal process is nigh impossible. The underlying mechanisms need to be agile & versatile enough to work on a massive variety of inputs. Traditional & formal algorithms do not work when it comes to facial recognition.

And that is where machine learning & computer vision steps in. But, before we dwell on the systems powering digital facial recognition systems, let’s go over the basic mechanisms behind face perception.

How We Recognise Faces? The Steps Towards Algorithmizing The Natural Process

In recent times, the system of face-selective regions in the human brain has been defined with unnatural precision using functional MRI (Magnetic Resonance Imaging). This makes it possible for researchers to approach the entire process in a hierarchical & mechanistic manner.

A research paper from the National Centre For Biotechnology Information offers a simplistic overview of how computer vision systems perceive faces.  

  • Detection

The most fundamental aspect of facial perception is simply detecting the presence of a face.

  • Once a vision system detects a face, it can extract features familiar to other faces. The T-shaped facial structure (eye, ears, nose, mouth) is an effective and ubiquitous schematic that simplifies any face detection process.
  • However, the article also states that face detection & identification have opposing demands. Detection requires extracting what’s familiar to all faces, while acute identification involves in-depth analysis to remove aspects unique to each face.

A suitable detector should be a poor individual recognizer and vice versa.

  • Detection vs Identification

Detection and identification should remain separate processes. This is because a facial detection mechanism can work as a domain-specific filter, activating identification & recognition systems only when the stimulus passes the threshold of a face. Such domain-specific gating is also evident in the neural pathways of our brains and allows for the anatomical segregation & categorization of different primate species.

Preceding identification by detection has another significant advantage. Detection automatically achieves facial segmentation by isolating faces from the background clutter and aligning an input to a particular standard template. Unfortunately, many facial recognition systems require prior segmentation & alignment and may fail without so.

  • Measurement & Categorization

Once a system detects a face, it needs to be measured minutely for accurate & efficient identification. In addition, the measurement process should be able to catch & extract subtle feature information that distinguishes one face from another.

At the same time, it should be able to output a set of values that can be effectively compared with stored templates & corpora.

The more accurate & efficient the measurement & analysis, the better the categorization & classification.

The above steps are intrinsic to almost every facial recognition system available today. However, face detection, feature extraction, and face recognition generally involve vast amounts of variable data. This is the reason why representation learning techniques are employed in this domain.

Using machine learning & computer vision, facial recognition systems find widespread applications across sectors. Here’s a look at the most prominent among them all.

Typical Applicational Areas Of Face Recognition

  • Entertainment

Video games, virtual reality, training simulations, man-machine interaction

  • Smart Systems

Immigration, identification, welfare fraud, voter registration

  • Information Security

Personal logon systems, application & database security, internet & network access, securing private & medical records

  • Law Enforcement & Surveillance

Advanced video surveillance, tracking & monitoring, CCTV control, investigation

Let us now dwell a bit deeper into a trendy facial recognition technology that employs artificial neural networks to detect, identify & categorize faces.

Facial Recognition Using Neural Networks

Before we get down to it, a brief intro about artificial neural networks is necessary.

Neural networks abstract the human brain’s neural network from the perspective of information processing & technology. It establishes a simplistic model and develops different networks according to connecting methods. They primarily consist of many interconnected nodes, each having a specific activation function.

Connections between any two nodes represent a weighting value for passing a connection signal. This signal is known as the weight and represents the memory of the neural network. The eventual output depends on the network architecture, the weight value, and the excitation function.

Artificial neural networks are approximations of some algorithms, functions, or logic strategies.

Building A Facial Recognition Model With Convolutional Neural Networks

Convolutional neural networks are a particular branch of ANNs inspired by the biological vision. They are essentially a forward feedback neural network, with the first few layers made up of a convolutional layer & a pooled layer cascaded alternatively to represent a simple cascade of simple & complex cells, similar to the visual cortex of the primate brain.

Currently, most facial recognition algorithms can be broadly divided into two categories.

  • Representation-Based Methods: Systems convert or map two-dimensional facial inputs into another space and use statistical methods to evaluate facial patterns. Typical methods used are Eigenface, Fisherface, and Support Vector Machines.
  • Feature-Based Methods: These methods generally extract local or global features and then use classifiers for face recognition.

A Convolutional Neural Network

CONVOLUTIONAL NEURAL NETWORKS-based methods for facial recognition fall under the feature-based category. Feature extraction in these models is done layer-by-layer using convolutional dimensional reduction and multi-layer nonlinear mapping. This allows the network to quickly learn from unprocessed training samples to form a feature extractor and classifiers that adapt to the recognition task.

A Barebones Description of A Facial Recognition Model

Convolutional neurons respond to a portion of the input from the previous layer, extracting higher-level features of the input. For example, a portion of the area is average or maximized to prevent any deformation or displacement in the information.

  • The Convolutional Layer: Convolutional layers extract primary visual features by employing simple methods of local connections & weight sharing. Each neuron in this layer is connected to neurons in the fixed area of the previous feature map.

Connections and weight sharing reduce the number of training parameters and act as a feature extractor. Local connections are realized as a convolution kernel during calculation.

  • The Pooling Layer: This layer simulates complex cells of the visual cortex. It samples, screens, and combines primary visual features into more abstract ones. After the pooling layer sampling, the size of the feature map becomes smaller, which reduces computational complexity and resists small changes to the input.
  • The Fully Connected Layer Of The Model: Models using the CNN approach extract features using at least four feature extraction layers and then employ a fully connected network layer. Each neuron of the layer is connected with all the neurons in the previous layer.

See Also – Finance Assignment Help

And that finally brings us to the end of this write-up. Hope it was an informative & engaging read for one & all.

Machine learning and computer vision are challenging & complicated domains. If you find things too demanding, seek machine learning and/or IT assignment help from reputed services.

All the best!

Author-Bio: Hank Atkins is a computer vision engineer at a major software development firm in London, the UK. He also teaches students part-time at MyAssignmenthelp.co.uk, a leading make my assignment & essay help service.

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