Discover the world of face recognition!
Over the past fifty years, the unstoppable acceleration of technological development has completely transformed society. The hyper-connectivity enabled by the deployment of communications networks is allowing the digitisation of a significant part of our activities, bringing numerous benefits as well as some risks that need to be addressed. In this analogue-digital duality, one of the processes that remains key to providing security is identity verification.
In the physical world, this is a task we carry out on a daily basis intuitively, recognising people at a glance. When this same (by no means trivial) activity must be carried out automatically without human intervention, it is necessary to deploy all technological potential to obtain reliable results.
In this sense,?biometrics in conjunction with artificial intelligence technologies form a robust team that allows us to provide security to digital processes where it is necessary to verify a person’s identity with guarantees.
The aim of this article is to provide an informative review of the fundamental aspects of facial recognition systems, helping to understand their key points and most interesting applications as well as the most relevant challenges we have to address.?
What is face recognition and what is it for?
In general terms,?face recognition can be defined as the process of automatically identifying the identity of a person by analysing an image of his or her face. This process encompasses a whole series of intermediate tasks, from the capture of the digital image to the final decision on the analysed identity.
To cover all workflow stages it is necessary to make use of different technologies, among which biometrics and machine learning are key elements:
Face recognition: A bit of history
It is not easy to define a single milestone for the beginning of the research and development of face recognition solutions, but?there is some general consensus to consider Woodrow Wilson Bledsoe’s work in the early 1960s as the starting point.
His approach consisted of recording the spatial coordinates of facial points of reference on a RAND tablet in such a way that a face was characterised by a set of numerical data. When performing an identification process, the biometric features of the input image were compared with those previously stored in the database, so that it was possible to return the one that had the closest similarity.
Although the results were obviously limited by the capabilities of the hardware at the time, these studies determined that facial biometrics was a useful method for identity verification and laid the foundations for the basic workflow in a biometric system: capture, modelling and matching.
Over the next twenty years, progress was slow, limited to an increase in the number of facial features extracted (always anthropometric in nature). It was not until the late 1980s when it was possible to model a facial image in a more robust way.
Sirovich and Kirby?applied linear algebra methods to achieve low-dimensional facial representations, i.e. to reduce the most important features of a face to a small set of numerical values.
This work was extended by?Turk and Pentland?to apply it to the face detection process, thus opening the door to a fully automatic face recognition process (without the need to provide a previously cropped facial image).
From that moment on, the research activity focused on two main lines: achieving robust facial descriptors and developing?machine learning?algorithms that would make it possible to find patterns and criteria for?distinguishing?between these characteristics in order to differentiate between identities.
Facial recognition systems?were successfully deployed in situations where it was necessary to automatically verify a person’s identity (such as access control), as well as serving as an alternative to the use of traditional passwords.
Yet, the game changer in terms of?the?significant increase in the performance of biometric systems has come from the recent boom in?deep learning, representing a real paradigm shift and almost unanimous adoption by the industry.
From an ontological point of view, deep learning algorithms are a subset of machine learning, comprising various techniques that seek to obtain high-level abstractions by analysing complex relationships between a large set of input data. A fundamental element of deep learning are?neural networks, algorithms with a long history that have achieved full currency thanks to the exponential increase in computational capacity and the availability of vast amounts of data.
It is at this point where we must place the current State of the Art, so the description of the details of the inner workings of a face recognition system that we will carry out use in the following sections will be done from this perspective.
The core of a face recognition system
As noted above, from a functional point of view, a face recognition system contains a number of distinct phases that allow?the process?to?be?successfully completed:
1. Capture:?the beginning of the workflow is to obtain the basic information. In our case, this information is just the face portion of the input image, so the first automatic process will be face detection.
In recent years, intensive work has been carried out in the field of face detection algorithms,?currently obtaining really high effectiveness rates even in the most demanding environments: artificial lights, very distant positions, use of glasses or masks… The result of this functional block will be a crop of the area of interest from the input image so that the system can work from that moment onwards exclusively with the relevant information (the facial area).
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2. Processing:?in any system based on machine learning, the processing is a fundamental stage. The objective is to normalise the input data within pre-established parameters so that the performance of the subsequent algorithms is optimal.
Machine learning systems use the conclusions drawn from the analysis of the training set to make decisions about new examples in production environments. For this reason, the input data must move within parameters similar to those used during training, avoiding extreme cases that could lead to undesired results.
In the case of a face recognition system, the processing consists of normalising the input image from the point of view of numerical values (illumination, colour deviations, range of coding values…) as well as content (centring the position of the face around a symmetry axis thanks to the localisation of key facial points).
3. Modelling:?this is the key operation?in?the overall process as it transforms an input image (the output of the processing block) into a set of numerical values commonly known as a feature vector. A feature vector could be understood?to be?a robust encoding of the most significant aspects of a face that differentiates it from other faces.
Artificial intelligence algorithms analyse complex patterns in the input data to locate the features with the highest discriminative power and use them to model an input image.
Currently, the feature extraction process is mostly carried out by deep learning algorithms (usually?convolutional network?architectures), as they are able to find very complex (non-linear) relationships in the input data, far outperforming manual feature extractors used in the previous generation.?
4. Comparison:?once the feature vector associated?with?a facial image is obtained, it is possible to undertake the comparison process. The objective is to determine the resemblance (in numerical terms) between faces in order to implement one of the following biometric operations:?Biometric Verification, Biometric Identification?or?Biometric Matching:
Matching:?are they the same person??Given two input images, the objective is to determine the match between them. A common use?of this operation is in remote customer registration systems, where the image of an identity document is compared with an image taken from the user during the registration process.
5. Decision making:?the normalised numerical value obtained at the output of the comparison block may not be very descriptive and therefore needs to be contextualised. The objective of the final decision-making stage is to return an interpretable response from this numerical value. This response can be a binary value (identity verified/unverified) or a label with the identity associated with the input user in the case of identification.
To make these decisions it is necessary to establish decision thresholds on the numerical values of the comparison in order to achieve a balance between security (very strict thresholds, a very high degree of confidence in the resemblance is needed) and usability (more relaxed thresholds, a lower degree of confidence is needed). Decision thresholds need to be adapted to the needs of each project by assessing the cost of a higher incidence of misclassification errors:
Use of face recognition technology
A biometric system based on?face recognition has multiple applications where it is required to verify the identity of users in digital environments. These are some of the most interesting and widely adopted by the industry:
Authentication beyond passwords
Historically, passwords have been the most widely used authentication method to verify people’s identities in digital environments. We are all forced to remember different sequences of characters that prove that we are authorised to carry out different operations: bank account management, access to email, access to private environments, etc. This requires us to use passwords that, in order to be secure, must be complex and frequently updated.
Indeed, this is its weakest point. For convenience reasons,?it is common to use the same password for different services or to use a simple and easy-to-remember one, increasing the possibility of being hacked and used for fraudulent purposes.
Through its application in identity verification systems, biometrics simplifies and strengthens the process, moving from something we know (the password) to something we are (our biometric features).?The integration of facial recognition systems in business solutions brings interesting benefits?in three main areas: