Mitek systems acquires ICAR, a CVC spin off focused in digital identity verification

Mitek, a global leader in mobile capture and digital identity verification software solutions, today announced that it has acquired ICAR, a leading provider of consumer identity verification solutions in Spain, for an aggregate purchase price of up to €12.75 million (~US$15.0 million) paid in a combination of cash and shares of Mitek common stock.

The acquisition of ICAR strengthens Mitek’s position as a global digital identity verification powerhouse in the Consumer Identity and Access Management (CIAM) solutions market, which the April 2017 MarketsandMarkets report, Consumer IAM Market – Global Forecast,  estimates will reach US$16.6 billion by 2022.

Headquartered in Barcelona with offices in Madrid, São Paulo, and Mexico City, ICAR was founded in 2002 as a spin-off of the Computer Vision Center of the Universitat Autónoma de Barcelona. Today, ICAR is a digital consumer identification leader in Spain and Latin America. ICAR’s channel distribution partners include Accenture, Informática El Corte Inglés, and other top technology solutions providers. Its customers are premier banks including Caixa Bank, Banco Neon, and Bancoppel, as well as companies in the telecom, insurance, travel, and energy sectors.

“The technical and cultural fit between ICAR and Mitek is a tremendous opportunity to maximize value for shareholders, while expanding our mission to bring the highest quality user experience and digital identity verification solutions to our customers globally,” said James B. DeBello, Chief Executive Officer of Mitek and Chairman of the Mitek Board of Directors.

Xavier Codó, Chief Executive Officer of ICAR added, “This combination provides a major growth opportunity and allows us to extend cloud services to our partners and customers in Spain and Latin America. It gives us the ability to offer our customers comprehensive identity document coverage throughout the U.S. and Europe. Mitek’s financial stability and commitment to research and development will also enable us to develop the industry’s most comprehensive, versatile, and advanced digital identity verification platform.”

“We have integrated ICAR’s automated identity identification solution. We view Mitek as a strong, established identity verification solutions provider and believe the combination will deliver additional capabilities to our platform giving us a competitive edge,” said Joan Manuel Tabero, CIO of Consumer Finance at Caixa Bank.

Following the acquisition, Mitek will offer extensive identity document coverage in North America, Europe, and Latin America. ICAR will increase Mitek’s digital identity verification capabilities with several new factors of authentication. The acquisition also further enhances Mitek’s desktop capture capabilities, which will enable customer on-boarding and authentication using computers in addition to mobile devices.

ICAR’s computer vision experts are tightly aligned with the Computer Vision Center of the Universitat Autónoma de Barcelona and dedicated to ongoing research and development. The merging of these experts with the Mitek Labs’ machine learning and computer vision scientists will create one of the most powerful research and development teams in the digital identity verification industry.

About ICAR 
The Spanish company, headquartered in Barcelona and with offices in Madrid, São Paulo, and Mexico City, was founded in 2002 as a spin-off of the Computer Vision Center of the Universitat Autónoma de Barcelona (UAB). ICAR provides cutting-edge technology solutions for customer identity verification, document forgery prevention, and fraud risk mitigation. ICAR’s fully automated verification solutions (desktop app, mobile and cloud-based tools) provide maximum security and an optimized user experience. ICAR’s technology is currently facilitating more than 20 million identity validations per year.

About Mitek
Mitek  is a global leader in mobile capture and identity verification software solutions built on the latest advancements in AI and machine learning. Mitek’s identity verification solutions allow an enterprise to verify a user’s identity during a digital transaction. This enables financial institutions, payments companies and other businesses operating in highly regulated markets to mitigate financial risk and meet regulatory requirements while increasing revenue from digital channels. Mitek also reduces the friction in the users’ experience with advanced data prefill and automation of the onboarding processes. Mitek’s innovative solutions are embedded into the apps of more than 5,800 organizations and used by more than 80 million consumers. For more information, visit www.miteksystems.com or www.miteksystems.co.uk.

 

The first computer algorithm comes by the name of Ada

Today is Ada Lovelace’s Day, a special celebration to visualize Ada’s contributions and to recognize women in science, technology, engineering and maths. Ada Lovelace made her contribution  in a moment when mathematics was considered only a man’s subject, but despite that, she created the first algorithm to be carried out by a machine. To commemorate her date, let’s deepen into this incredible figure’s life:

Science and poetry influences

Ada Lovelace was Lord Byron’s daughter, one of Britain’s  greatest poets, and Anne Isabella Milbanke divorced only a few weeks after Ada’s birth. Ada grew up close to her mother, who introduced her in maths and science through private tutors in order to avoid her following Lord Byron’s romantic ideals. Right from the beginning, Ada Lovelace showed a great talent for mathematics and a notorious intelligence.  Despite Lady Byron’s efforts to keep Ada away from poetry, she was always inclined to  integrating science and poetry in her work. Her understanding of mathematics was highly imaginative and she had the habit of describing this discipline through metaphors.  Because of this,  she was known as “The Enchantress of Numbers”. As to her father,  she never saw him again after he and her mother signed the divorce. Still, he dedicated her some of his verses.

Essential contributions

Ada Lovelace was born in London 1815, under the influences of the Victorian Society, certainly not an age in which women could undertake a scientific career. Most women would be schooled in arts and social grades. Instead, Ada was tutored in maths and science despite society’s prejudices, all inspired by the character of a visionary mother.

During her teens, she worked with the notorious mathematician and astronomer Mary Sommerville and they translated Laplace’s work together. It was in that period when she also met Charles Babbage, who is considered as the father of the computer, and thus became her mentor. In that age, Babbage wanted to create the analytical engine, a machine with which people could process mathematical calculations. Ada translated to English the sole document related to that mechanism including some personal annotations in which she showed the first algorithm specifically tailored for implementation on a computer. Although the analytic engine was never completed and Ada’s program was never tested, with this contribution, Ada Lovelace became the  world’s first computer programmer, although this recognition would not arrive until many years later.

Ada’s legacy

Ada was the first person to imagine the amount of possibilities that modern computers held. She anticipated programming processes, with a firm belief that that computers would be able to do whatever we wanted them to do, if we knew how to correctly implement these orders within them. Her predictions were accurate reflections of what later became and turning her into a true visionary.

Her work and contributions did not receive any recognition until one century after her death. Her work had been highly advanced to the age she had lived in. Her work was not valued or understood until one century later, when technology progressed and her findings were found astonishingly correct.

During the 1970’s, the United States Department of Defence created a computer language which they named Ada in honour to Lovelace’s contributions within the computing community. Nowadays, this language is still used for a high number of applications, especially in aviation systems.

Announcement of the awards of GIANA Sub-challenge on Gastrointestinal Image Analysis

GIANA 2017 Sub-challenge on Gastrointestinal Image Analysis took part on September 10th during MICCAI 2017 conference, held in Quebec, Canada. It was part of the MICCAI 2017 EndoVis challenge, which comprised 4 different categories this year: Gastrointestinal Image ANAlysis, Surgical Workflow Analysis in the SensorOR, Robotic Instrument Segmentation and Kidney Boundary Detection.

GIANA 2017 Sub-challenge was jointly organized by 4 different institutions from Spain and France: Computer Vision Center/Universitat Autònoma de Barcelona (CVC-UAB), ETIS laboratory at Ecole nationale supérieure de l’Electronique et de ses Applications (ETIS-ENSEA), Hospital Clinic de Barcelona (HCB) and Hôpital Saint-Antoine AP-HP (HAS). Main organizers of the sub-challenge were Jorge Bernal from CVC-UAB and Prof. Aymeric Histace from ETIS-ENSEA.

GIANA Sub-challenge is the continuation of previous challenges (ISBI 2015 and MICCAI 2015) on polyp detection. This year the sub-challenge consisted of three main different tasks: polyp detection and localization in videocolonoscopy, polyp segmentation in colonoscopy images and angiodysplasia detection and localization in wireless capsule endoscopy images. Completely new databases and annotations were provided for each of the tasks thanks to the collaboration with our clinical partners, led by PhD MD Gloria Fernández-Esparrach from HCB and PhD MD Xavier Dray from HAS.

A total of 57 different research teams were interested in the different data provided in the sub-challenge and finally, 12 of them took part in the actual challenge: 1) TU/e  – VCA research group from Technology University of Eindhoven (TU/e-VCA), 2) CUEndo from The Chinese University of Hong Kong (CUEndo), 3) National Institute of Informatics Shin’ichi Satoh Laboratory (NII-Satoh), 4) Laboratoire d’Informatique de Paris 6 (LIP6), 5) Computer Vision and Machine Learning group from University of Central Lancashire (CVML), 6) Norwegian University of Science and Technology (NTNU), 7) University College of London (UCL), 8) GastroView group from Politechnika GDanska (GastroView), 9) The Medical Image Analysis Lab at Simon Fraser University  (SFU), 10) TrueAccord and Massachusets Institute of Technology (TA-MIT), 11) CMEMS group from University of Minho (UMinho), 12) Konica Minolta (KM).

Teams had access to annotated training data from June 5th and testing data was released on July 20th. Final submission data of the results was August 29th. All information related to the challenge was released in GIANA official website though data was only accessible after registering on the website and acceptance of the rules for participation.

Awards for the challenge were sponsored by Société Française d’Endoscopie Digestive (SFED), which provided with 12 different prizes for a total of 2000 €. The following categories were defined: 1) Polyp Detection and Localization, 2) Polyp Segmentation (SD and HD images), 3) Angiodysplasia Detection and Localization and 4) Overall Participation award, which considered only the teams that took part in all 3 previous categories.

With respect to Polyp Detection and Localization category, the first prize (250 €) was awarded to TA-MIT team, composed by Vladimir Iglovikov and Alexey Shvets. Second prize (150 €) was awarded to UCL team composed by Patrick Brandao and Danail Stoyanov. Third prize (100 €) was awarded to NTNU team composed by Younghak Shin, Hemin Ali Qadir and Ilangko Balasingham.

Winners of Polyp Detection and Localization awards. From left to right, Vladimir Iglovikov (TA-MIT, 1st prize), Danail Stoyanov (UCL, 2nd prize) and Younghak Shin (NTNU, 3rd prize).

The winner of the Polyp Segmentation category (250 €) was CVML team, composed by Yun Bo Guo, Pedro Henriquez and Bogdan J. Matuszewski. Second prize (150 €) was awarded to SFU team composed by Saeed Izadi and Ghassan Hamarneh. As a result of a tie in the results, third prize (100 €) was awarded to UCL team composed by Patrick Brandao and Danail Stoyanov and to TA-MIT team, composed by Vladimir Iglovikov and Alexey Shvets.

Winners of Polyp Segmentation awards. From left to right, Bogdan J. Matuszewski (CVML. 1st prize), Aïcha Bentaieb (SFU, 2nd prize), Vladimir Iglovikov (TA-MIT, 3rd prize) and Danail Stoyanov (UCL, 3rd prize).

The winner of the Angiodysplasia Detection and Localization category (250 €) was TA-MIT team, composed by Vladimir Iglovikov and Alexey Shvets. Second prize (150 €) was awarded to to NTNU team composed by Younghak Shin, Hemin Ali Qadir and Ilangko Balasingham. Third prize (100 €) was awarded to TU/e-VCA team composed by Farhad Ghazvinian Zanjani and Joost van der Putten.

Winners of Angiodysplasia Detection and Localization awards. From left to right, Vladimir Iglovikov (TA-MIT, 1st prize), Joost van der Putten (TU/e-VCA, 3rd prize) and Younghak Shin (NTNU, 2nd prize).

Finally and with respect to the Overall Participation award, only two teams took part in all the different tasks of the challenge therefore only two awards were given. First prize (250 €) was awarded to TA-MIT team, composed by Vladimir Iglovikov and Alexey Shvets and second prize (150 €) was awarded to UCL team composed by Patrick Brandao and Danail Stoyanov.

Winners of the Overall Participation task. From left to right, Vladimir Iglovikov (TA-MIT, 1st prize) and Danail Stoyanov (UCL, 2nd prize).

We want to acknowledge the quality of the contributions from all the teams. Slides presented in the challenge detailing the results, along with details of each of the methodology, will be published later this week. Additionally, we aim to publish journal publications summarizing the results of the challenge. We also want to deeply thank the main organizers of EndoVis challenge (Stefanie Speidel, Lena Maier-Hein and Danail Stoyanov) for their help and support during challenge preparation. We look forward to organize future iterations of this sub-challenge in the following years.