A Bibliometric Analysis on Face Recognition Technology Research

Cloud computing involves multidisciplinary development and integration, and is the result of information technology and related applications. In this study, Web of Science Core Collection database is used as data source. Using Excel, Bibexcel, VOSviewer and CiteSpace software for data mining and quantitative analysis of the 2005–2018 year research papers on cloud computing, we explore the trend of change, the distribution of research forces, key areas, research hot spots and international cooperation. The results show that the number of documents in cloud computing has been growing, and there are many subjects involved in the research of cloud computing technology, mainly Computer Science, Engineering and Telecommunications. There are many research papers on cloud computing in China and the United States; China is most prominent in international cooperation and has strong cooperative relations with the major countries; Chinese Academy of Sciences is very active in the research of cloud computing, and its papers are far ahead of other institutions. The main focuses of the study are virtualization, mobile cloud computing, big data, security, etc. The main hot spots of research are mobile cloud computing, big data, fog computing, secure, storage, access control, sever consolidation, etc.

The ability to recognize facial expressions of emotion is vita l for effective social interaction. Facial expressions convey emotional cues (Adolphs, 2002, 2003; Darwin, 1872/1965; Ekman, 1992, 1993) and accurate recognition of these cues is a necessary step in the evaluation of interpersonal interactions and for the subsequent application of appropriate social skills (Collins & Nowicki, 2001; Hampson, van Anders, & Mullin, 2006; Philippot & Feldman, 1990). Research has shown support for the association between facial expression recognition (FER) abilities and social competency (Custrini & Feldman, 1989; Lancelot & Nowicki, 1997; Nowicki & Duke, 1994); relationship difficulties (Barth & Bastiani, 1997; Blair & Coles, 2000; Lancelot & Nowicki, 1997); and various psychological and psychiatric conditions (Blair et al., 2004; Penn & Combs, 2000; Russell, Chu & Phillips, 2006; Silver, Goodman, Knoll, & Isakov, 2004; van Beek & Dubas, 2008), including anxiety (Easter et al., 2005), bipolar disorder (Brotman et al., 2008), and psychopathology (Blair et al., 2004). Other studies have shown support for associations between FER deficits and childhood maltreatment (Pears & Fisher, 2005; Pollak, Cicchetti, Hornung, & Reed, 2000) and attachment (Cooley, 2005; Magai, Distel & Liker, 1995; Niedenthal, Brauer, Robin & Innes-Ker, 2002). According to emotions theorist Izard (2002), the inability to recognize nonverbal forms of emotion expression can negatively affect intra-and interpersonal behavior, and may serve as a risk factor for poor adjustment and future adverse outcomes. Although the particular mechanisms underlying FER are under debate, the study of the ability to recognize facial expressions of emotion continues to be the subject of research within various fields, including psychology (Hall, 2006; Ekman, Friesen, & Tomkins, 1971; Nowicki & Carton, 1992), psychiatry (Herba & Phillips, 2004; Wang, Lee, Sigman, & Dapretto, 2007), as well as computer science and philosophy (Adolphs, 2002). Facial expression recognition research findings have been provided from varied perspectives, such as neuroscience (Wang et al., 2007); social-cognitive processing and early childhood experiences (Masten et al., 2008; Mullins & Duke, 2004; & Pollack & Sinha, 2000); as well as perception (Pollak, Messner, Kisler, & Cohn, 2009). Other researchers have integrated information from differing fields, such as social psychology and perception (Calder, Young, Keane, & Dean, 2000; Pollak et al., 2009) and behavior studies and neuroscience (Herba & Phillips, 2004). Thus, facial recognition research appears to be bridging some of the natural gaps caused by distinct fields of investigation. The authors of this paper will attempt to present findings from FER studies that are particularly relevant for behavioral psychologists. Following a brief history of the study of FER, we highlight findings from various fields (e.g., social psychology, nonverbal processing), that focused on FER with particular clinical populations. Discussion of suggested clinical implications and applications from a clinical behavioral perspective, and suggestions for future research directions, concludes this article on the importance of FER in clinical work. History of Facial Expression Recognition Study Current research and public interest in facial expression recognition stems from a rich history. Scientific study and understanding of emotion is thought to have begun in the 19th century with Charles Darwin's The Expression of the Emotions in Man and Animals (originally published in 1872) and G.G. Duchenne de Bologne's The Mechanism of Human Facial Expression (originally published in 1862) (Mayne & Bonanno, 2001). These early works focused on the important role of facial displays in emotional life and introduced the theory that emotions may be understood as biologically-based reflex behaviors serving adaptive functions. …

Background: Metal-organic frameworks (MOFs) are hybrid materials composed of metal ions or clusters and organic ligands that spontaneously assemble via coordination bonds to create intramolecular pores, which have recently been widely used in biomedicine due to their porosity, structural, and functional diversity. They are used in biomedical applications, including biosensing, drug delivery, bioimaging, and antimicrobial activities. Our study aims to provide scholars with a comprehensive overview of the research situations, trends, and hotspots in biomedical applications of MOFs through a bibliometric analysis of publications from 2002 to 2022. Methods: On 19 January 2023, the Web of Science Core Collection was searched to review and analyze MOFs applications in the biomedical field. A total of 3,408 studies published between 2002 and 2022 were retrieved and examined, with information such as publication year, country/region, institution, author, journal, references, and keywords. Research hotspots were extracted and analyzed using the Bibliometrix R-package, VOSviewer, and CiteSpace. Results: We showed that researchers from 72 countries published articles on MOFs in biomedical applications, with China producing the most publications. The Chinese Academy of Science was the most prolific contributor to these publications among 2,209 institutions that made contributions. Reference co-citation analysis classifies references into 8 clusters: synergistic cancer therapy, efficient photodynamic therapy, metal-organic framework encapsulation, selective fluorescence, luminescent probes, drug delivery, enhanced photodynamic therapy, and metal-organic framework-based nanozymes. Keyword co-occurrence analysis divided keywords into 6 clusters: biosensors, photodynamic therapy, drug delivery, cancer therapy and bioimaging, nanoparticles, and antibacterial applications. Research frontier keywords were represented by chemodynamic therapy (2020-2022) and hydrogen peroxide (2020-2022). Conclusion: Using bibliometric methods and manual review, this review provides a systematic overview of research on MOFs in biomedical applications, filling an existing gap. The burst keyword analysis revealed that chemodynamic therapy and hydrogen peroxide are the prominent research frontiers and hot spots. MOFs can catalyze Fenton or Fenton-like reactions to generate hydroxyl radicals, making them promising materials for chemodynamic therapy. MOF-based biosensors can detect hydrogen peroxide in various biological samples for diagnosing diseases. MOFs have a wide range of research prospects for biomedical applications.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence, a general class of recognition models has emerged, which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity. However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ 'featurally' are much easier to distinguish when inverted than those that differ 'configurally'; a finding that is difficult to reconcile with the physiological models. Here, we show that after controlling for subjects' expectations, there is no difference between 'featurally' and 'configurally' transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in the cortex.

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service, which, in turn, has raised significant concerns on privacy as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secure face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, we develop a low-complexity encrypting algorithm based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. We further prove that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity, we extract deeper features in an intermediate space, expanded according to the dictionaries from each device, and perform classification in this new feature space to combat the noise and modeling error.

Facial recognition is a challenging area of research due to difficulties with robust face recognition (FR) under occlusion and sparse representation-based classification (SRC) only focusing on face global features. To solve these issues, we proposed an occluded FR method based on dictionary learning for sparse representation and sub-classifiers fusion (LSSRC), which efficiently combines local and overall characteristics of face images. First, we partitioned continuous but non-lapped blocks of the face by multi-resolution blocking. Then, for each block, SRC was used for feature extraction and face classification. We established a sub-block dictionary and conducted K-SVD dictionary learning, established sub-classifiers and determined weight. Finally, we conducted sub-classifier fusion recognition using voting rules with weight. Results using the AR and YaleA database showed that our algorithm achieved superior recognition performance to the existing sparse representation classification occluded FR method.

Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

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

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

From the past two decades, various spheres of activity are in the aspect of ‘Face recognition’ as an essential tool. The complete series of actions of face recognition is composed of 3 stages: Face Detection, Feature Extraction and Recognition. In this paper, we make an effort to put forth a new application of face recognition and detection in education. The proposed system scans the classroom and detects the face of the students in class and matches the scanned face with the templates that is available in the database and updates the attendance of the respective students.

Face recognition is one of the most important tools of identification in biometrics. Face recognition has attracted great attention in the last decades and numerous algorithms have been proposed. Different researches have shown that face recognition with Sparse Representation based Classification (SRC) has great classification performance. In some applications such as face recognition, it is appropriate to limit the search space of sparse solver because of local minima problem. In this paper, we apply this limitation via two methods. In the first, we apply the nonnegative constraint of sparse coefficients. As finding the sparse representation is a problem with very local minima, at first we use a simple classifier such as nearest subspace and then add the obtained information of this classifier to the sparse representation problem with some weights. Based on this view, we propose Weighted Non-negative Sparse Representation WNNSR for the face recognition problem. A quick and effective way to identify faces based on the sparse representation (SR) is smoothed $L_0$-norm $(SL_0)$ approach. In this paper, we solve the WNNSR problem based on the $SL_0$ idea. This approach is called Weighted Non-Negative Smoothed $L_0$ norm $(WNNSL_0)$. The simulation results on the Extended Yale B database demonstrate that the proposed method has high accuracy in face recognition better than the ultramodern sparse solvers approach.

Recently Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition. In SRC, the testing image is expected to be best represented as a sparse linear combination of training images from the same class, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. However, SRC emphasizes the sparsity too much and overlooks the spatial information during local feature encoding process which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the spatial information but overlooks the different discriminative ability in different face regions. In this paper, we propose to weight spatial locations based on their discriminative abilities in sparse coding for robust face recognition. Specifically, we learn the weights at face locations according to the information entropy in each face region, so as to highlight locations in face images that are important for classification. Furthermore, in order to construct a robust weights to fully exploit structure information of each face region, we employed external data to learn the weights, which can cover all possible face image variants of different persons, so the robustness of obtained weights can be guaranteed. Finally, we consider the group structure of training images (i.e. those from the same subject) and added an l2,1-norm (group Lasso) constraint upon the formulation, which enforcing the sparsity at the group level. Extensive experiments on three benchmark face datasets demonstrate that our proposed method is much more robust and effective than baseline methods in dealing with face occlusion, corruption, lighting and expression changes, etc.