Threat response: a compelling application for wearable computing

I. Introduction Advances in wearable electronics, functional nanocomposite polymers, flexible microfluidics, and commercial polymer microfluidics are reported on a daily basis. However, much of this research is field-specific, despite the enormous potential that the convergence of these research areas has for the future of health care, worker safety, food safety, consumer devices, and commercial microfluidics. We present an innovative approach of powerful, general-purpose frameworks that facilitate interfacing of nanocomposite polymer-based, microfluidic, and electronic devices on flexible polymer, textile, or industrial polymer platforms. Such approaches offer practical solutions for areas such as safety lighting and health monitors for safety vests; heart monitors and perspiration monitors for athletic clothing; and other applications in real-time wearable bioelectric and biochemical monitoring. II. Wearable Microfluidic and Electronic Frameworks A. Wearable bioelectric sensors Intense research into wearable electronics has resulted in many innovative devices and systems, e.g.: flexible polyimide or Kapton printed circuit boards (PCBs) [1]; roll-to-roll foil and other printed devices [2]; printing and weaving of textiles [3]; and special geometries for flexible interconnect between rigid components [4]. We have investigated conductive nanocomposite polymers and metal transfer processes for wearable bioelectric sensors [5, 6]. These technologies can be employed in, for example, conformable and wearable systems for electrocardiogram (ECG) [5], tissue impedance [7] and pressure [8] sensors. Unlike many other techniques, the technology and materials employed for our devices are highly compatible with clothing-based textiles. B. Wearable microfluidics Much less research has been performed in the emerging area of wearable microfluidics. Traditionally, microfluidic devices are fabricated in rigid substrates or flexible materials such as polydimethylsiloxane (PDMS) that are bonded to rigid substrates. Free-standing PDMS devices are developed for, e.g., perspiration sensors [9]; however, such processes typically require long fabrication times, equipment in a cleanroom facility, and difficulty with integration onto textiles. Other devices employ porous materials or microneedles to deliver biofluids, e.g., perspiration or interstitial fluid, by capillary forces [10]; however, such devices are limited in fluid collection or are too invasive. Wearable textile-based devices have been recently demonstrated that use the textile itself as a fluidic “channel” [11]; however, such devices are dependent on the fluidic characteristics of the fabric. To overcome these limitations and facilitate the development of fully flexible, wearable, and durable microfluidic devices that can be used for a wider variety of applications, we have developed a printing-based fabrication process that employs screen printable plastisol ink [12]. This process is demonstrated for simple multi-level microfluidic devices toward the goal of fully wearable microfluidic systems. III. Summary Many methods have been developed for microelectronic and microfluidic devices in rigid or flexible packages (e.g., [13, 14, 15]). However, such technologies suffer from difficult integration and/or limited flexibility, limited geometries, limited microfluidic sample size, and/or difficult integration, especially on textile-based substrates. The technologies we present offer simple and compatible printing-based and transfer processes for microelectronic and microfluidic frameworks that facilitate cost-effective development of durable wearable biomedical systems. References Y. Chuo, et. al., IEEE Transactions Biomedical Circuits Systems 4(5):281-94 (2010).K. Jain, et. al., Proceedings of the IEEE 93(8):15000-10 (2005).F. Carpi, et. al., IEEE Transactions Information Technology Biomedicine 9(3):295-318 (2005).M. Gonzalez, et. al., Microelectronics Reliability 51:1069-76 (2011).D. Chung, et. al. Proc. SPIE 9060, (2014); doi:10.1117/12.2046548.D. Hilbich, et. al., Proc. SPIE 98020R (2016); doi:10.1117/12.2219284.D. Chung, et. al., J Electrochemical Society 161(2):B3071-76 (2014).A. Rahbar, et. al., Proc. SPIE 90600O (2014); doi:10.1117/12.2044342.J. Choi, et. al., Adv Healthcare Mater 6:1601355 (2017).F. Benito-Lopez, et. al., Procedia Chemistry 1(1):1103-1106 (2009).A. Nilghaz, et. al., Lab Chip 12:209-218 (2012).D. Chung & B.L. Gray, J Micromech Microeng, 27(11), (2017).S. Cheng., et. al., Advanced Functional Materials 21(12):2282-90 (2011).M. Scholles, et. al., Proc. of SPIE 7593:75930C-2 (2013).A. Wu, et. al., Lab Chip 10:519-521 (2010).

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.

This paper describes the iCare Interaction Assistant, an assistive device for helping the individuals who are visually impaired during social interactions. The research presented here addresses the problems encountered in implementing real-time face recognition algorithms on a wearable device. Face recognition is the initial step towards building a comprehensive social interaction assistant that will identify and interpret facial expressions, emotions and gestures. Experiments conducted for selecting a face recognition algorithm that works despite changes in facial pose and illumination angle are reported. Performance details of the face recognition algorithms tested on the device are presented along with the overall performance of the system. The specifics of the hardware components used in the wearable device are mentioned and the block diagram of the wearable system is explained in detail.

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.

In this paper, it studies two critical technical links of face detection and face recognition in the recognition of face, and conducts analyses and comparisons on some commonly used face detections and recognition algorithms. Through the collation of this information and the combination of related knowledge in the digital image processing, it proposes a face recognition algorithm based on the singular value decomposition. After preprocessing face images, by the use of the projection method, it obtains positions of five sense organs, and then extracts local feature values near five sense organs as the main features of faces by using the singular value decomposition. For different face images of the same individual, the matching degree of feature values will be very high.KeywordsFace recognitionimage recognitionlocal informationfeature extraction

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

The last decade has seen automatic face recognition evolve from small-scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve verification accuracies comparable to those of fingerprint recognizers. In these experiments, only frontal face images taken under controlled lighting conditions were used. As the use of face recognition systems expands toward less restricted environments, the development of algorithms for view and illumination invariant face recognition becomes important. However, the performance of current algorithms degrades significantly when tested across pose and illumination, as documented in a number of evaluations. In this chapter, we review previously proposed algorithms for pose and illumination invariant face recognition. We then describe in detail two successful appearance-based algorithms for face recognition across pose, eigen light-fields, and Bayesian face subregions. We furthermore show how both of these algorithms can be extended toward face recognition across pose and illumination.

Facial recognition is a feature that uses facial detection algorithms to detect a face and then invokes facial recognition algorithms to try to match the person’s face. First, a person’s face needs to be enrolled; during enrollment, the person’s facial details are saved in a database—in this case, a mobile phone. The facial recognition algorithm uses this database to match the currently presented face with the faces saved in the database. The efficiency of a facial recognition algorithm depends upon the speed at which it can detect and recognize faces. The problem we address in this paper is that a reliable, automated method for testing facial recognition features in mobile phones. Because the multimedia capabilities of smartphones have expanded phenomenally, the need for thoroughly testing facial recognition algorithms has become crucial. The uses of facial recognition can range from facial authentication for unlocking phones to security for a number of other applications. To meet these needs, a reliable, automated test for validating the facial recognition algorithms must be developed. The challenge for the software test team was to automate the test cases, which involve tilting the phone at specific angles from the test subject. The permissible angular movements of the phone are determined by the algorithmic specifications of the facial recognition algorithm. We tested scenarios involving multiple faces, motion blur, panning the phone in front of the test subject faces at various speeds, and so on. We adopted a robotic arm to perform the facial recognition test cases and developed software to program the robotic arm to test the phone’s facial recognition software for functionality, performance, and stability as well as adversarial tests. This paper discusses the computer vision use case for facial recognition and describes how we developed an automated facial recognition test suite using a robotic arm.

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance “bar” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields “state-of-the-art” performance levels on a standard face recognition set (“labeled faces in the wild”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more “realistic” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life, for example, healthcare monitoring and treatment, ambient monitoring, soft robotics, prosthetics, flexible display, communication, human‐machine interactions, and so on. According to the development in recent years, the next‐generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence (AI) and internet of things (IoT), to achieve a higher level of comfort, convenience, connection, and intelligence. Herein, this review provides an opportune overview of the recent progress in wearable electronics, photonics, and systems, in terms of emerging materials, transducing mechanisms, structural configurations, applications, and their further integration with other technologies. First, development of general wearable electronics and photonics is summarized for the applications of physical sensing, chemical sensing, human‐machine interaction, display, communication, and so on. Then self‐sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage technologies. Next, technology fusion of wearable systems and AI is reviewed, showing the emergence and rapid development of intelligent/smart systems. In the last section of this review, perspectives about the future development trends of the next‐generation wearable electronics/photonics are provided, that is, toward multifunctional, self‐sustainable, and intelligent wearable systems in the AI/IoT era.image

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

As a brand-new computing mode, wearable computing is bringing forward new issues for computer science and technology, will form new computing concepts, theories and technologies, and will expand computing's functions and open new computing application areas. This paper mainly analyzed context aware technology of wearable computing, proposed the establishment of context aware technology based on active mechanism. Then the wearable computing system is able to perceive, understand and predict users' activities, and give them instant assistance.

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC) $^{2}\text{A}$ and Block FLDA.

Brain inspired face recognition: A computational framework