IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain

Unbewusste Modulatoren der somatosensorischen Wahrnehmung

It is intriguing that perception of the same stimulus can vary profoundly from trial to trial. For example, it has been shown in many studies that weak, so-called “near-threshold stimuli” are sometimes consciously perceived and sometimes not. In my thesis, I have been investigating factors which underlie this profound perceptual variability in the somatosensory domain. Together with my colleagues, I performed three studies in which we tested three different types of presumed non-conscious modulators of somatosensory perception. In the first – behavioral - study, we investigated how the presence of subliminal noise during a peripheral somatosensory stimulation influences perception. Counter-intuitively, we found that peripheral noise can even improve perception of weak somatosensory stimuli. In our interpretation, this occurs most likely due to “stochastic resonance” effects (Study I: Iliopoulos et al. 2014). In the second – behavioral and EEG - study, we tested the effect of different forms of pulsed subliminal stimulation (single pulses versus pulse trains) on brain rhythms and somatosensory perception. Following-up on previous results of our group, we tested the hypothesis that subliminal pulsed stimulation impairs perception of subsequent stimuli via centrally enhanced Mu rhythm. Interestingly, the main result of this study was that trains of subliminal stimuli indeed inhibited subsequent somatosensory detection, however, - in contrast to our previous findings for single pulses – trains were associated with decreased Mu rhythm. We conclude that central rhythms most likely play a role in mediating the perceptual modulation of peripheral subliminal stimuli, however, the relationship is more complex than previously assumed (Study II: Iliopoulos et al. 2020). In the third study, we examined the influence of interoceptive signaling, especially from the heart, on somatosensory perception. The hypothesis was that the cardiac phase (systole versus diastole) and the so-called heart-evoked potential (HEP) would modulate somatosensory perception. Indeed, our study showed that somatosensory perception was better during diastole than during systole and detection performance declined as the amplitude of the HEP increased. Our interpretation of the former effect assumes that all events which occur simultaneously with the “pulse” are assumed by the brain to be pulse-synchronous peripheral noise and therefore suppressed. Our interpretation of the latter effect (HEP) assumes that HEP is a marker of the relative balance between interoception and exteroception (Study III: Al et al. 2020). In conclusion, in the studies which form the basis for my thesis, we have shown that somatosensory perception is modulated by peripheral effects (modes of peripheral stimulation, peripheral noise), central effects (Mu rhythm) and interoceptive signals from the heart. The precise interplay between these modulators is an exciting research topic for future studies.Interessanterweise kann die Wahrnehmung desselben Reizes von Augenblick zu Augenblick so stark variieren, dass dieser manchmal bewusst wahrgenommen wird und manchmal nicht. In meiner Dissertation habe ich Faktoren untersucht, die dieser Wahrnehmungsvariabilität im somatosensorischen (SS) System zugrunde liegen. Mit meinen Kollegen habe ich drei Studien durchgeführt, in denen wir verschiedene mutmaßlich unbewusste Modulatoren der SS-Wahrnehmung untersuchten. In der ersten Studie untersuchten wir, wie die Wahrnehmung peripherer SS-Reize durch unterschwelliges Rauschen beeinflusst wird. Wir konnten zeigen, dass peripheres Rauschen die Wahrnehmung schwacher Reize verbessert. Dies ist ein Hinweis auf das Vorliegen von "stochastischen Resonanzeffekten" (Studie I: Iliopoulos et al. 2014). In der zweiten Studie, die neben behavioralen Messungen auch elektroencephalographische (EEG) Messungen umfasste, testeten wir die Auswirkung verschiedener Formen gepulster unterschwelliger elektrischer Fingerstimulationen (Einzelpulse gegen Pulsserien) auf die Wahrnehmung und auf Hirn-rhythmen. Ausgehend von früheren Ergebnissen unserer Arbeitsgruppe überprüften wir, ob repetitive subliminale Stimulationen die Wahrnehmung nachfolgender Reize über einen zentral verstärkten Mu-Rhythmus beeinträchtigen. Das Ergebnis dieser Studie war, dass Serien unterschwelliger Reize tatsächlich die nachfolgende SS-Wahrnehmung hemmten, jedoch - im Gegensatz zu früheren Ergebnissen für Einzelimpulse – die Reizserien mit einem verringerten Mu-Rhythmus verbunden waren. Daraus schließen wir, dass zentrale Rhythmen höchstwahrscheinlich eine Rolle bei der Wahrnehmungsmodulation durch periphere unterschwellige Reize spielen, dass aber der Zusammenhang zwischen beiden komplexer ist als bisher vermutet (Studie II: Iliopoulos et al. 2020). In der dritten Studie untersuchten wir den Einfluss interozeptiver Signale aus dem Herzen auf die SS-Wahrnehmung. Die Hypothese war, dass die Herzphase und das so genannte Herz-evozierte Potenzial (HEP) die SS-Wahrnehmung modulieren. Wir zeigten, dass die SS-Wahrnehmung während der Diastole besser war als während der Systole und dass die Wahrnehmung in umgekehrtem Verhältnis zur Amplitude des vorausgehenden HEP stand. Für den ersten Effekt legen unsere Daten nahe, dass alle Ereignisse, die zusammen mit der Pulswelle auftreten, vom Gehirn als puls-synchrones peripheres Rauschen angenommen und daher unterdrückt werden. Der zweite Befund wird in Übereinstimmung mit der Literatur am besten dadurch erklärt, dass das HEP ein Marker für das relative Gleichgewicht zwischen Interozeption und Exterozeption darstellt (Studie III: Al et al. 2020). Zusammenfassend zeigen die Ergebnisse dieser Arbeit, wie die SS-Wahrnehmung durch periphere Effekte (Art der Stimulation, Rauschen), zentrale Effekte (Mu-Rhythmus) und interozeptive Signale des Herzens moduliert wird. Das genaue Zusammenspiel zwischen diesen Modulatoren ist ein spannendes Forschungsthema für zukünftige Studien

Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry

In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biometric authentication system based on convolutional neural networks. Contrary to previous approaches, our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns image in the (p, q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal are strongly dependent on the vascular bed's biostructure, which is unique to each individual, and highly stable over time and other psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the convoluted nature of the blood network. Our biometric authentication system reaches very low Equal Error Rates (ERRs) with a single attempt, making it possible, by the very nature of the envisaged solution, to implement it in miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

A NOVEL PERSONAL AUTHENTICATION USING KNUCKLE MULTISPECTRAL PATTERN

ABSTRACT With the increased use of biometrics for identity verification, there have been similar increases in the use of unimodal biometric system. The finger knuckle print recognition is one of the newest biometric techniques research today. In this paper, one of the reliable and robust personal identification approaches using finger knuckle print is presented. Many researchers are going on in face, finger print and iris recognition and which finds its usage in many applications. These biometric which find its usage in many applications are easily duplicated for fraudulent activities. But the finger knuckle print recognition is the unique pattern to identify the individuality at a high level of accuracy. This paper proposes new algorithms for finger knuckle print recognition using SIFT algorithm and this algorithm presents, extracting a new original constant features from images As the proposed method matches the different angles of finger knuckle print with the database, its reliability is very high when compared to other biometrics. The features of SIFT which are invariant to image scale and rotation, are shown to provide robust matching across a substantial range of fine distortion, change in 3D viewpoint, addition of noise, and change in illuminance. The features are highly distinctive, in the sense that a single feature could be correctly matched with high probability against a large database of features from many images

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area