After writing the previous blog post about face recognition, we decided to build a real-world face recognition project in Ars Futura – a face recognition door lock. Smart Lock, as we call it, recognises people based on their face and unlocks the door if the person works at Ars Futura. As you can see in the video above, we installed a front door camera that unlocks the door based on who is outside the office. I can proudly say that we have had Smart Lock running for over a year. After some initial hiccups, we managed to get it working pretty well.

Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer highly accurate commercial face recognition web services for diverse applications to meet the end-user needs. Naturally, however, such technologies are threatened persistently, as virtually any individual can quickly implement impersonation attacks. In particular, these attacks can be a significant threat for authentication and identification services, which heavily rely on their underlying face recognition technologies' accuracy and robustness. Despite its gravity, the issue regarding deepfake abuse using commercial web APIs and their robustness has not yet been thoroughly investigated. This work provides a measurement study on the robustness of black-box commercial face recognition APIs against Deepfake Impersonation (DI) attacks using celebrity recognition APIs as an example case study. We use five deepfake datasets, two of which are created by us and planned to be released. More specifically, we measure attack performance based on two scenarios (targeted and non-targeted) and further analyze the differing system behaviors using fidelity, confidence, and similarity metrics. Accordingly, we demonstrate how vulnerable face recognition technologies from popular companies are to DI attack, achieving maximum success rates of 78.0% and 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with any celebrity) attacks, respectively. Moreover, we propose practical defense strategies to mitigate DI attacks, reducing the attack success rates to as low as 0% and 0.02% for targeted and non-targeted attacks, respectively.

Our API provides face recognition, facial detection, eye position, nose position, mouth position, and gender classification. If you have any questions ask! Just send an email to [email protected] Happy Hacking! -Stephen Face (Detection) – A computer vision api for facial recognition and facial detection that is a perfect face.com We currently have a free api for face detection. Animetrics Face Recognition – The Animetrics Face Recognition API can be used to detect human faces in pictures.

Interesting listing published on Mashape. Only the top 12 are listed below. It would be nice to have a separate blog for voice recognition APIs. I've been thinking at using voice rather than passport or driving license, as a more secure ID. The voice has a texture unique to each individual.