Identity Recognition Research Articles

Enhancing Electrocardiogram Identity Recognition Using Convolutional Neural Networks With a Multisimilarity Loss Model

Enhancing Electrocardiogram Identity Recognition Using Convolutional Neural Networks With a Multisimilarity Loss Model

AI Based Speech Analysis Framework

The pioneering AI-Based Speech Analysis Framework presented in this research paper was painstakingly created to help people overcome linguistic obstacles, notably in the context of English language communication. Through a thorough speech analysis, the framework's multimodal approach enables real-time evaluation of emotional state, fluency, stress levels, and even identification recognition. This framework delivers a sophisticated and perceptive interpretation of spoken language by utilizing cutting-edge artificial intelligence approaches, hence promoting an enhanced and successful communication experience. The study is focused on four significant sub-objectives, each of which advances the main objective of encouraging increased self-awareness and communication: First, by detecting subtly emotional indicators embedded in the voice, the framework transforms emotional assessment. The AI algorithms identify emotional patterns, such as enthusiasm, trepidation, or tranquility. This in-the-moment emotional analysis creates opportunities for tailored communication techniques and a greater understanding of the speaker's feelings. The framework also introduces a novel way for assessing fluency levels using voice analysis. It analyzes various facets of speech, such as pace, intonation, and lexical decisions, giving language learners immediate feedback on their level of linguistic proficiency. This makes it easier to make focused improvements and to move more easily toward effective communication. The framework also discusses the complex relationship between stress and good communication. It measures stress levels through vocal pattern analysis, offering light on instances of heightened tension or anxiety when speaking. Such knowledge enables people to overcome stress-related hurdles and enhance communication. The framework's capacity to accurately identify people based on distinctive voice traits lies at the heart of its innovation. Language limitations are no obstacle to this identity recognition technology, which provides an effective and secure method of identification in a variety of settings. Voice-based identification detection accelerates procedures and promotes inclusion in a variety of settings, including work settings and public services. The development of an AI-Based Speech Analysis Framework that reveals fresh angles in language evaluation and communication improvement is the culmination of this research. It not only encourages self-improvement but also highlights the revolutionary potential of AI in redefining language landscapes and promoting true connections by merging emotional, fluency, stress analysis, and identity identification through voice.

Research on the Scientific Issues of Identity Recognition of Image Traces

Research on the Scientific Issues of Identity Recognition of Image Traces

Fusion recognition of palmprint and palm vein based on modal correlation.

Biometric authentication prevents losses from identity misuse in the artificial intelligence (AI) era. The fusion method integrates palmprint and palm vein features, leveraging their stability and security and enhances counterfeiting prevention and overall system efficiency through multimodal correlations. However, most of the existing multi-modal palmprint and palm vein feature extraction methods extract only feature information independently from different modalities, ignoring the importance of the correlation between different modal samples in the class to the improvement of recognition performance. In this study, we addressed the aforementioned issues by proposing a feature-level joint learning fusion approach for palmprint and palm vein recognition based on modal correlations. The method employs a sparse unsupervised projection algorithm with a "purification matrix" constraint to enhance consistency in intra-modal features. This minimizes data reconstruction errors, eliminating noise and extracting compact, and discriminative representations. Subsequently, the partial least squares algorithm extracts high grayscale variance and category correlation subspaces from each modality. A weighted sum is then utilized to dynamically optimize the contribution of each modality for effective classification recognition. Experimental evaluations conducted for five multimodal databases, composed of six unimodal databases including the Chinese Academy of Sciences multispectral palmprint and palm vein databases, yielded equal error rates (EER) of 0.0173%, 0.0192%, 0.0059%, 0.0010%, and 0.0008%. Compared to some classical methods for palmprint and palm vein fusion recognition, the algorithm significantly improves recognition performance. The algorithm is suitable for identity recognition in scenarios with high security requirements and holds practical value.

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Abstract In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.

Total Variation PCA-Based Descriptors for Electrocardiography Identity Recognition

Total Variation PCA-Based Descriptors for Electrocardiography Identity Recognition

An ECG Deep Learning user identification architecture using ECG sex recognition as a selective parameter

Background:Human user authentication can be implemented by token-, keyword-, or identity-based mechanisms for digital environment session entry (i.e., smartphones, platforms with log-in). Physiological signals, such as ECG, have shown discriminative properties for user identity recognition. Due to ECG hidden nature, it is resilient to public trait exposition, light/noise saturation, or eavesdropping in contrast to fingerprint, facial, voice, or password approaches. ECG might fill those gaps toward a cooperative authentication environment. Methods:This paper proposes a Deep Learning identification scenario in which the inclusion of sex recognition directs the input sample toward a sex-specialized identity classification model, simplifying the discrimination space for each model. The architecture proposed could be suitable for large populations. Our scheme worked with an ECG three-axis pseudo-orthogonal configuration in which each axis is transformed into a time-frequency space. Additionally, we combine each lead matrix in an RGB image, joining the contribution of each wavelet waveform. Results:Our results suggest that it is possible to identify people by using RGB wavelet representations, achieving a classification average of 99.97%. In addition, the inclusion of the sex category for our identification purpose does not significantly affect the classification performance, making it a feasible solution for systems with a larger population. Conclusions:With the features of our database, we have evidence that it is possible to recognize a person’s identity using an ECG sex recognition module through our proposed architecture.

Wi-Fi-Based Personnel Identity Recognition: Addressing Dataset Imbalance With C-DDPMs

Wi-Fi-Based Personnel Identity Recognition: Addressing Dataset Imbalance With C-DDPMs

Embracing Diversity: Advancing Transgender Mental Health in Nepal

The understanding and recognition of transgender identities have evolved over time and reflected in changes to diagnostic classifications.

Changes of Identity Recognition in the Internet Age and Its Impact

In modern digital era, the Internet has transformed the way individuals perceive and construct their identities. This essay delves into the profound impact of the Internet on identity formation and interpersonal relationships, focusing on its "strangeness". The essay uses a case study, explaining the phenomenon of online identity experimentation among Chinese college students. The concept of "strangeness" online challenges traditional relationship dynamics. It empowers them to forge novel virtual identities, transcending geographical and social boundaries. However, this newfound freedom also gives rise to challenges, as division between online and offline personas can lead to societal conflicts. A pivotal study on Chinese college students sheds light on the intricate relationship between online identity experimentation and self-identity. The research highlights the influence of online social tendencies and compulsive Internet use on this connection. In summary, the Internet is offering opportunities for self-discovery and connection while posing challenges related to authenticity and the "strangeness" of online interactions. This essay reminds people that as the internet continues to evolve, it shapes our identities and relationships, emphasizing the need for a balanced approach in this digital age.

Enhancing Individual Worker Risk Awareness: A Location-Based Safety Check System for Real-Time Hazard Warnings in Work-Zones

This study focuses on improving pre-emptive risk recognition and safety checks to prevent workplace accidents. It underscores improvements by addressing existing research issues, suggesting potential enhancements through system development. Work approval procedures and workers’ prior risk awareness, through the confirmation of work safety standards in physically separated work areas, are fundamental methods of preventing serious accidents and creating a safe work environment. A key factor concerning worker safety is recognizing the potential accident risk factors (or hazards) in advance through practical job hazard analysis and consequently take risk-reduction measures in case the safety standards are not met. Despite the crucial significance of pre-awareness of work risks at the majority of construction sites, tools to enhance this awareness are currently limited. Furthermore, real-time detection of work risks and the implementation of risk reduction measures are contingent upon a monitoring environment and a robust safety culture. This study proposed construction worker location-tracking technology that recognizes personal identification (ID). A safety check system based on location tracking combining personal quick response code (QR code) recognition and computer vision technology to automatically identify workers’ personal identities and track their physical location was proposed. A real-time safety check system was implemented to classify automatically whether workers have confirmed hazards and to approve a work process in high-risk workplaces by supervisors. Location-tracking technology with ID recognition performed the following two safety management functions. First, if a construction worker does not pre-check the work risk information before entering the work zone, the geofencing technology automatically classifies workers as those who are not aware of job hazards. Secondly, the safety manager or supervisor entering the on-site work zone possesses the authority to halt work if the work environment fails to meet safety standards and can issue warnings regarding risky situations. Essential functions were validated through a case study involving preliminary testing within the development system. To assess the practical application of the system, virtual simulations were conducted using recorded videos from a construction site to replicate the two essential functions of the system. The system was constructed using an Apache server and Python code, and for testing purposes, the names of the workers were randomized.

EEG Topography Amplification Using FastGAN-ASP Method

Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The development of biometric identity recognition based on EEG signals has significantly improved the security and accuracy of biometric recognition. However, EEG signals obtained from incompatible acquisition devices have low universality and are prone to noise, making them challenging for direct use in practical identity recognition scenarios. Employing deep learning network models for data augmentation can address the issue of data scarcity. Yet, the time–frequency–space characteristics of EEG signals pose challenges for extracting features and efficiently generating data with deep learning models. To tackle these challenges, this paper proposes a data generation method based on channel attention normalization and spatial pyramid in a generative adversative network (FastGAN-ASP). The method introduces attention mechanisms in both the generator and discriminator to locate crucial feature information, enhancing the training performance of the generative model for EEG data augmentation. The EEG data used here are preprocessed EEG topographic maps, effectively representing the spatial characteristics of EEG data. Experiments were conducted using the BCI Competition IV-Ⅰ and BCI Competition IV-2b standard datasets. Quantitative and usability evaluations were performed using the Fréchet inception distance (FID) metric and ResNet-18 classification network, validating the quality and usability of the generated data from both theoretical and applied perspectives. The FID metric confirmed that FastGAN-ASP outperforms FastGAN, WGAN-GP, and WGAN-GP-ASP in terms of performance. Moreover, utilizing the dataset augmented with this method for classification recognition achieved an accuracy of 95.47% and 92.43%.

Konstruksi Pertimbangan Hakim: Pengakuan Identitas Jenis Kelamin Seorang Transgender Dalam Fase Sintonik

The societal stance on transgender individuals elicits various reactions, ranging from acceptance to rejection. This, in turn, has a significant impact on the discriminatory treatment that gender reassignment applicants receive from the community. Generally, individuals who seek gender reassignment do so due to discomfort with their gender identity. As such, transgender individuals strive to assert their right to change their gender. In Indonesia, the legal basis for gender reassignment is not positive, as there is no specific regulation governing gender reassignment. However, Article 56 of Law Number 24 of 2013, which amends Law Number 23 of 2006 concerning Population Administration, indirectly provides an opportunity for transgender individuals to apply for a change in their gender status through a court decision. Essentially, judges cannot dismiss a case due to a lack of legal basis and must instead create laws by exploring the existing laws in society. This is what is referred to as "judge-made law." The recognition of transgender individuals' gender identity in the Wates District Court provides a legal basis for gender identity rights. This research uses a normative research method with a legislative and conceptual approach. Qualitative research data is used to collect literature study data. The research aims to determine the legal basis for the Wates District Court's consideration in determining gender reassignment cases and to analyze the case's decision based on the legal basis's value. The study's results show that the Wates District Court considers the applicant's background, physical and psychological condition, and request for sex reassignment when determining gender reassignment cases.

A Comprehensive Survey on Wi-Fi Sensing for Human Identity Recognition

In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and identity recognition, among others. This paper summarizes and analyzes the research of Wi-Fi sensing technology in human identity recognition. Firstly, we overview the history of Wi-Fi sensing technology, compare it with traditional identity-recognition technologies and other wireless sensing technologies, and highlight its advantages for identity recognition. Secondly, we introduce the steps of the Wi-Fi sensing process in detail, including data acquisition, data pre-processing, feature extraction, and identity classification. After that, we review state-of-the-art approaches using Wi-Fi sensing for single- and multi-target identity recognition. In particular, three kinds of approaches (pattern-based, model-based, and deep learning-based) for single-target identity recognition and two kinds of approaches (direct recognition and separated recognition) for multi-target identity recognition are introduced and analyzed. Finally, future research directions are discussed, which include transfer learning, improved multi-target recognition, and unified dataset construction.

Current Approaches to Heritage Spanish and the Identity Construction of Spanish Heritage Speakers: Lessons Learnt from Five European Countries

An individual’s social identity, often overlooked in Europe in the field of Spanish as a second or foreign language (S2L/SFL), has always been the focus of attention in the teaching of heritage Spanish in the USA, especially in programmes designed from critical pedagogy and based on a reconstructive narrative of Latino immigration. There, heritage speakers (HS) strengthen their identity as linguistic experts and contribute to positive social change that counteracts the scholastic subordination of Spanish to English in primary schools. In this research based on verified questionnaires, we investigate in the European context (Italy, Poland, Germany, Portugal, and Belgium) how primary–middle school HSs attending extracurricular S2L/SFL classes self-perceive their identity in comparison to S2L/SFL students. The results show that the incidence of the factor “country of origin” is central to identity recognition and highlight the feelings linked to different classroom conditions and dynamics for heritage and S2L/SFL students. To conclude, these results are contrasted with those obtained in the United States.

The Imprisonment and Escape of the Characters in The Cement Garden from the Perspective of Power Space

Under the interaction of space and power, characters in The Cement Garden are subject to the double restriction of the above-ground and underground spaces in their home, resulting in confused identity cognition, which leads to the characters’ incestuous behavior. From the perspective of power space, this paper explores the negative impact of family dysfunction on the soul and body of characters, as well as the initiative and necessity of individuals to develop a clear personal identity recognition through interaction with social space.

Research on apple identity recognition method based on hyperspectral technology and chemometrics method

This study focuses on using hyperspectral technology and chemometric methods to identify apples from four production areas and ten varieties to improve the accuracy of apple identification. Physical and chemical data and hyperspectral images of apple samples were collected for preprocessing and feature selection of spectral data. In the next step, K-nearest neighbour algorithm (KNN) and the support vector machine-radial basis kernel function (SVM-RBF) models were established. Regarding the identification of apple varieties based on origin information, research was conducted for both the same and different origins. For the same origin modeling, the best results were obtained from the KNN model, with accuracy, F1-score, and recall all reaching 100% for both the modeling and prediction sets. The PLS-DA prediction results were slightly inferior to KNN, and SVM modeling produced the worst results. The PLS-DA model produced the best results for predicting different apple origins, with accuracy, F1-score, and recall all achieving over 98% for both the modeling and prediction sets. Regarding the identification of apple origin based on variety information, research was conducted for both the same and different varieties. The best model for identifying the origin of apples with the same variety was SD-PLS-DA, with accuracy, F1-score, and recall of 97.38%, 99.59%, and 99.60% for the modeling set, and 99.61%, 97.35%, and 97.84% for the prediction set. For different varieties, both the KNN and PLS-DA models were effective, with the KNN model outperforming the PLS-DA model, and all indicators reaching 100% for both the modeling and prediction sets. The results indicated that the use of variety and origin identification models in this study was effective for apple identification, and can accurately identify apples.

The Kurdish Conflict in Turkey: The Central Role of Identity Recognition (or Lack Thereof)

Academics and state officials over the years have put forth a host of factors to explain armed insurrections in Turkey since the 1920s. From ‘religious reactionaries’ and ‘underdevelopment’ to ‘foreign intervention,’ ‘economic inequality’ and ‘identity-based grievances.’ What sort of explanation one subscribes to generally seems to depend on either one’s politics or simply which analysis sounds most plausible and convincing. This study looks for evidence from the insurrections in Turkey to help shed more light on the issue. We examine the stated demands and grievances of the rebels fighting the Turkish state in the major uprisings of the twentieth century (1925, 1927–1930, 1937 and 1978 onwards). Judging from their own statements, the recognition of Kurdish identity was central to all these episodes. No other issue appears so prominently in all the uprisings. The study then turns to theoretical work on protracted conflicts and ethnic conflicts to explain why identity recognition is of such importance. Following this analysis, parameters to resolve the Kurdish conflict in Turkey are examined in light of these observations. Solutions to end the conflict need to begin first and foremost with official identity recognition and acceptance of the Kurds, as Kurds. The rest consists of issues to be negotiated.

Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation.

(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach which involves mixed feature sampling, sparse representation, and recognition. (2) Methods: This paper introduces a new method of identifying individuals through their ECG signals. This technique combines the extraction of fixed ECG features and specific frequency features to improve accuracy in ECG identity recognition. This approach uses the wavelet transform to extract frequency bands which contain personal information features from the ECG signals. These bands are reconstructed, and the single R-peak localization determines the ECG window. The signals are segmented and standardized based on the located windows. A sparse dictionary is created using the standardized ECG signals, and the KSVD (K-Orthogonal Matching Pursuit) algorithm is employed to project ECG target signals into a sparse vector-matrix representation. To extract the final representation of the target signals for identification, the sparse coefficient vectors in the signals are maximally pooled. For recognition, the co-dimensional bundle search method is used in this paper. (3) Results: This paper utilizes the publicly available European ST-T database for our study. Specifically, this paper selects ECG signals from 20, 50 and 70 subjects, each with 30 testing segments. The method proposed in this paper achieved recognition rates of 99.14%, 99.09%, and 99.05%, respectively. (4) Conclusion: The experiments indicate that the method proposed in this paper can accurately capture, represent and identify ECG signals.

“I Am Not Just a Man”: Chinese Butterfly’s Identity Anxiety and Ethical Predicament in David Henry Hwang’s M. Butterfly

M. Butterfly is narrated through the memory of Western white man Rene Gallimard, which makes the audience focus on Gallimard’s behaviors and ignore the Oriental man Song Liling’s emotions, actions, and choices. However, there are many plots portraying Song as a Chinese Butterfly to deconstruct the stereotype of Madame Butterfly. This paper, from the perspective of ethical literary criticism, probes into Song’s brain texts formed in their growth and working experience and analyzes Song’s anxiety and confusion about his multiple and chaotic ethical identities, so as to demonstrate that Song is not a “dragon lady” or a “transvestite”. In China, Song is feminized and marginalized in society as “a son of a prostitute”, “a gay”, and “an Opera actor”. He wants to change the situation, so he becomes “a spy” for the Chinese government and “a lover” for Gallimard. Still, due to the failure of ethical enlightenment in childhood and the cruel social environment in China, Song is trapped in ethical predicaments of “to be or not to be”, struggling in the ethical conflicts between the honor of the individual and the interest of the nation, as well as between the desire for love and the mission from government. The song is a victim of the era and politics, and his identity anxiety and ethical predicament reflect David Henry Hwang’s position as an Asian American playwright and reveal his ethical appeal for gender equality, identity recognition and cultural confidence.