With more than 100 years of combined research experience in the field of Machine Learning and Artificial Intelligence, we have some of the best solutions in the world, like Apparent Age Estimation System, Facial Recognition System, Facial Landmarks Detection System and state of the art Cross-Model Person Re-Identification System.
We have some of the best solutions in the world, like Facial Recognition System, Facial Landmarks Detection System, Human Age Estimation System and state of the art Person Re-Identification System.
Our solution is a fully automated age estimation system. The backbone of our system is a deep neural network which produces state-of-the-art results on several benchmarks. The system performs best with front-facing shots and is more accurate than existing systems such as Amazon, IBM, Microsoft. Read More..
Our face recognition system is the state of the art piece of technology which can detect all the faces in the live/offline video feed and compute unique facial features for each one of them. We also maintain different watch lists and can accurately match each person's identity. Read More..
This product presents an advanced tracker continuously tracking arbitrary objects in videos. Given the bounding box of any object in any video our artificially intelligent algorithm will track that object through the rest of the video. Read More..
Our solution is able to detect human faces, track facial key points (eye corner, nose tip, mouth corner etc.) and reconstruct 3D face shapes in real time. This technology has many practical applications in security systems, digital entertainment, human-computer interaction, augmented reality and virtual reality. Read More..
Given a picture of a person, the aim of Person Re-ID is to identify the same person across different cameras. Often done with images and videos, this product also uses natural language description of a person to spot the person of interest more reliably. This can be helpful in many security applications such as identifying a criminal from a witness description. Read More..
Our product accurately detects 68 landmaks on human face. This product is building block of many interesting products in the field of biometrics and and computer animations.
Josef is cofounder of Sensus Futuris and the Fellow of the Royal Academy of Engineering (FREng). He is also Distinguished Professor at University of Surrey. He founded Centre for Vision, Speech and Signal Processing (CVSSP) in 1986 at University of Surrey and served as President of the International Association for Pattern Recognition during 1994–1996. He is Series Editor of Springer Lecture Notes in Computer Science.
Muhammad is a cofounder and CTO of Sensus Futuris and a senior machine learning researcher in the University of Surrey Centre for Vision, Speech and Signal Processing (CVSSP). He works on cutting edge facial analysis algorithms and co-supervises PhD students and early career research fellows. He was also co-founder of CerebrAI, an AI start-up focusing on online smart retail. He won a grant from Ignite National Technology Fund Pakistan of $300,000 for CerebrAI to do R&D on “Deep Learning for Smart Retail”.
David McIntosh has over 40 years’ successful experience in business leadership, organisational development, management and application of research. Over the last eighteen years he has specialised in the leadership of technologically advanced image processing businesses, including broadcast electronics, video-analytics, biometrics and feature-rich database software.
Dr Chi Ho Chan is working on Face Identification and Verification. He has more than 15 years experience in Machine Learning and AI.
Ali Akbari received the MSc degree in Electrical Engineering from the Shiraz University of Technology, Iran in 2012 and the PhD degree in Telecommunications from the Sorbonne Université, Paris, France in March 2018. Since July 2018, he joined the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK as a research fellow to enrich his experiences in the field of face recognition. He has published two book chapters and several papers in peer-reviewed journals and conference proceedings. He has served as an Associate Editor for the IEEE Open Journal of Circuits and Systems. His research interests include computer vision, deep learning, dictionary learning and image and video coding.
Dr. Safwan did his bachelors in Electrical Engineering from NUST, Pakistan, MSc in Mobile and Satellite Communications from University of Surrey, UK and then PhD in Bayesian Machine Learning from COMSATS University, Pakistan. His current research activity is focused on low resolution face recognition using deep neural networks and research interests include Machine Learning, Deep Learning, Computer Vision and Statistical Signal Processing.
15 years experience in full-stack software development. Transforming cutting edge Machine Learning algorithms in to real-world software applications.
Means "Sensing the Future"
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