Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Extremely Low Quality Image Face Recognition

Aastate jooksul on piltide töötlemine ja analüüs arenenud pakkudes nüüd igapäevastele väljakutsetele praktilisi lahendusi. Uute lahenduste ja ettepanekute sünd toob kaasa ka uusi väljakutseid, mis on paratamatult seotud innovaatiliste uuendustega. Olemasolevad näotuvastuse algoritmid on hästi toiminud ja neid on muu hulgas rakendatud sellistes lahendustes nagu sotsiaalmeedia kujutise märgistamine, mobiiltelefoni näo biomeetriline autentimine ja sisserände piirikontrolli näotuvastus. Põhjus miks need algoritmid on suutnud eelnimetatud stsenaariumides hästi toimida tuleneb sellest, et kasutuskõlblike kujutiste kvaliteet on tavaliselt kõrge eraldusvõimega [1].Teistes näidetes kus näotuvastus vajalikuks osutub nagu linna turvakaamerad, lennujaama kaamerad ja muud situatsioonid kus kujutise salvestuskvaliteeti ei saa kontrollida või manipuleerida, muutub jõulisema lahenduse leidmine pea kohustuslikuks, et oleks võimalik nägu tuvastada sõltumata kaadri suurusest, valgusoludest, rassist, vanusest, kehaasendist või muudest varieeruvatest faktoritest, mis võivad oluliselt muuta algoritmide võimet kujutistest aru saada.Käesoleva töö eesmärk on tuvastada ja testida alternatiivseid meetodeid näotuvastusülesannete täitmiseks äärmiselt madala kvaliteediga piltides.Image processing and analysis have evolved over the years into providing practical solutions to everyday challenges. The birth of new solutions and proposals also create new challenges usually surrounding the new innovations.Existing face recognition algorithms have performed well and they have been deployed into solutions such as social media image tagging, mobile phone facial bio-metric authentication, immigration border control face matching among other solutions. The existing algorithms have been able to perform well in these scenarios because of the quality of the image from these use cases are usually of high quality with high resolution (HR) [1]. In other possible application of face recognition such as city camera surveillance, airport security surveillance and other related scenarios where image stream quality cannot be directly controlled or manipulated, it becomes imperative to seek a more robust solution that can deal with face recognition regardless of the frame size, lighting condition, race, age, pose and other varying factors that can significantly change the way the images are perceived by existing algorithms.The goal of this thesis is to identify and test alternative methods of performing face recognition task in extremely low-quality images

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa