UNIT Art Lab: A look at technology through art

Since primitive times, art and its many expressions have been tools to examine, analyze and record what happens around us. In a similar way, science has contributed tremendously to the understanding of different phenomena throughout history. However, each of these disciplines seemed to always work as opposites. Well, until now. This is when Art Lab comes in.

The subjective dimension of art and the rigorous nature of science are the two natures that come together in UNIT Art Lab. The project is promoted by UNIT, a company dedicated to the development of artificial intelligence solutions and aims to generate new views on technology and the fate of life on Earth.

Along these lines, digital platforms offer us the possibility of creating imaginary spaces or environments that traditional art has barely managed to explore. This is why they are the protagonists of this project.

To achieve this, data analysis tools are used in a crossover with media arts techniques. The result is different visualizations and figures that the visual artist Sergio Mora-Díaz created from the data of more than 600 patients, who were monitored for two years to evaluate the evolution of their blood based on the INR indicator of blood coagulation. This, from the VOYAGER project promoted by UNIT.

“My artistic work is closely linked to space and, mainly, to the generation of experiences. Being able to discover new technologies to propose new types of sensory experiences is a great opportunity of which I am very happy to be a part of”, explains Mora-Díaz.

"Much of my work is based on the use of algorithms, that is, mathematical data, to be able to create geometric figures or interactive environments. For example, through sensors capable of capturing information from the environment and translating it into light, sound or image”, the artist points out.

“Universal intelligence includes art as its most influential means of expression, since it connects and articulates, in different ways, creative thinking, vision and intimate sensations of the world around us. In this way, Art Lab allows a further arrival in the transcendent thinking of our community, complementing our rational base of analytical tools and software”, assures Juan Larenas, CEO of UNIT.

Open call

You are an artist? Would you like to be part of this experiment? Find more information in the following link.



AvatarCarla Espinoza Gutiérrez

Journalist and Community Manager. I am passionate about delivering the right message for different kinds of audiences, always evolving and refreshing the tools, shape and tone of delivery.

Face Recognition: a constantly updated technology

Face recognition refers to the technology capable of identifying the identity of subjects in images or videos. It is a non-invasive biometric system, where the techniques used have varied enormously over the years.

During the 90's, traditional methods used handcrafted features, such as textures and edge descriptors. Gabor, Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Scale Invariant Feature Transform (SIFT), etc. are some examples of this, which were the basis for more complex representations, through coding and transformation of characteristics such as Principal Component Analysis (PCA), LCA, among others. Aspects such as luminosity, pose or expression can be managed through these parameters.

In the past, there was no technique that could fully and comprehensively master all scenarios. One of the best results achieved is the one presented in the study "Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification", where 95% is achieved in the Labeled Face in the Wild (LFW) database. This indicates that the existing methods were insufficient to extract a representation of the faces that was invariant to the changes of the real world.

How does facial recognition work today?

In recent years, traditional methods have been replaced by others based on deep learning, which in turn have their origin in Convolutional Neural Networks (CNN). The main advantage of methods based on deep learning is that they can “learn”, from large databases, the best characteristics to represent the data, that is, to build the faces.

An example of this is the DeepFace network, which in 2014 achieved a “state of the art” performance in the famous LFW database. With this, he was able to approximate the performance of a human in an unrestricted scenario (DeepFace: 97.35% vs Humans: 97.53%). This, training a 9-layer model on 4 million images of faces. Inspired by this work, the focus of the research shifted towards methods based on deep learning, reaching 99.8% in just three years.

Facial recognition systems are usually made up of the stages shown in the following figure:

Face recognition systems. (a) Face detection. (b) Alignment of the face. (c) Representation of the face. (d) Face matching.
  1. Face detection: A query image is entered into the system. A detector finds the position of the face in the query image and returns the coordinates of the position.
  2. Face Alignment: Your goal is to scale and crop the image in the same way for all faces, using a set of reference points.
  3. Representation of the face: The pixels of the image of the face image are transformed into a compact and discriminative representation, that is, into a vector of characteristics. This representation can be achieved using classical methods or models based on deep learning. Ideally, all images of the faces of the same subject should have vectors with similar characteristics.
  4. Face matching: The images of the faces of registered individuals make up a database called a gallery. Each face image in the gallery is represented as a feature vector. Most methods calculate the similarity between the feature vector in the query image and the vectors in the gallery, using the cosine distance or the L2 distance. The one with the smallest distance indicates to which individual the consulted face belongs.



Cristóbal QuezadaCristóbal Quezada

I am a Data Scientist interested in natural language processing.