User Recognition Research Articles

Development of a user-following mobile robot with a stand-up assistance function

In this paper, a user-following mobile robot which tracks and follows the user, offering stand-up assistance function is proposed. The proposed robot plays the role of a chair where the user can sit on, and offers a stand-up assistance function compensating the lack of muscle strength. In the proposed robot, a sensing method for buttocks recognition using a depth sensor is proposed. By measuring the distance from the user’s buttocks, the walking state is recognized and the tracking is performed at a fixed distance. As an approach to realize the tracking function, a human tracking method for mobile robots using PD control is constructed. According experimental study, usefulness of the proposed mobile robot with the function of user-following and stand-up assistance is confirmed. The user recognition method and the tracking method using PD control are confirmed effective. With the proposed robot system, improvement in welfare field can be expected.

A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection

The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature.

Design and simulation of AI remote terminal user identity recognition system based on reinforcement learning

Presently, the design process of the AI remote server can enable the user to evaluate whether an authorized user can gain emotional responses when they establish an emotional product interface in the approach of the interaction with the device. Therefore, it is necessary for user experience and the ability to address the user’s emotional expectations. This paper proposes an artificial intelligence-based user face recognition response system (AI-UFRRS) to monitor users’ emotions in real life continually and provide new insights into their emotional responses and transitions. The user face recognition response system design is analyzed based on device intelligence. Eventually, the response system is improved and strategy based on an intelligent device. The proposed AI-UFRRS utilizes reinforcement learning technologies to maintain emotional processing in substantial information relating to the user’s identity. This paper offers AI remote strategies to reduce identification information and maximize information on emotions formed by reinforcement learning. The results suggest that the system provided can perform a convolute transformation to maintain user recognition accuracy and reduce face identity recognition. Thus, the experimental results of AI-UFRRS show the improved accuracy ratio of 95.6%, the recognition rate of 93.4%, emotion ratio of 95.5%, high response system ratio of 96.3%, and to increase user identification ratio of 91.8% and reduced false acceptance rate of 19.2%, the false rejection rate of 19.5% compared to other methods.

RESEARCH INFORMATION SYSTEM OF IMAGE RECOGNITION

Data classification in presence of noise can lead to much worse results than expected for pure patterns. In paper was investigated problem of the research is the process of user recognition and identification in the video sequence. The main contributions presented in this paper are experimental examination of influence of different types of noise and to the increase the security of an IT company by developing a computer system for recognizing and identifying users in the video sequence. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. However, the training of classifiers is very slow, but the face search results are very fast.

Emotion Detection using Social Media Data

Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost‐effective human‐machine interface to enhance the immersion during the long‐term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG‐based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower‐limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real‐time robotic manipulation and virtual game for immersion‐enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower‐limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming‐aided training, showing its potential in IoT‐based smart healthcare applications.

The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities

Building on the elaboration likelihood model (ELM) and absorptive capacity, this study develops a four-dimensional model of idea adoption in Virtual Crowdsourcing Communities (VCCs) and examines the influence of different persuasion cues on idea adoption. The research model was tested using hierarchical logistic regression based on a dataset from the Tableau community. The results show that both community recognition of users and community recognition of ideas are positively related to idea adoption. Proactive user engagement has a significant positive impact on idea adoption, while reactive user engagement has no significant impact. Idea content quality, represented by idea length and supporting arguments, has an inverted U-shaped relationship with idea adoption. Community absorptive capacity positively moderates the curvilinear relationship between idea content quality and idea adoption. These results contribute to a better elucidation of the persuasion mechanisms underlying idea adoption in VCCs, and thus provide important implications for open innovation research and practice.

Reverberation Degrades Pitch Perception but Not Mandarin Tone and Vowel Recognition of Cochlear Implant Users.

The primary goal of this study was to investigate the effects of reverberation on Mandarin tone and vowel recognition of cochlear implant (CI) users and normal-hearing (NH) listeners. To understand the performance of Mandarin tone recognition, this study also measured participants' pitch perception and the availability of temporal envelope cues in reverberation. Fifteen CI users and nine NH listeners, all Mandarin speakers, were asked to recognize Mandarin single-vowels produced in four lexical tones and rank harmonic complex tones in pitch with different reverberation times (RTs) from 0 to 1 second. Virtual acoustic techniques were used to simulate rooms with different degrees of reverberation. Vowel duration and correlation between amplitude envelope and fundamental frequency (F0) contour were analyzed for different tones as a function of the RT. Vowel durations of different tones significantly increased with longer RTs. Amplitude-F0 correlation remained similar for the falling Tone 4 but greatly decreased for the other tones in reverberation. NH listeners had robust pitch-ranking, tone recognition, and vowel recognition performance as the RT increased. Reverberation significantly degraded CI users' pitch-ranking thresholds but did not significantly affect the overall scores of tone and vowel recognition with CIs. Detailed analyses of tone confusion matrices showed that CI users reduced the flat Tone-1 responses but increased the falling Tone-4 responses in reverberation, possibly due to the falling amplitude envelope of late reflections after the original vowel segment. CI users' tone recognition scores were not correlated with their pitch-ranking thresholds. NH listeners can reliably recognize Mandarin tones in reverberation using salient pitch cues from spectral and temporal fine structures. However, CI users have poorer pitch perception using F0-related amplitude modulations that are reduced in reverberation. Reverberation distorts speech amplitude envelopes, which affect the distribution of tone responses but not the accuracy of tone recognition with CIs. Recognition of vowels with stationary formant trajectories is not affected by reverberation for both NH listeners and CI users, regardless of the available spectral resolution. Future studies should test how the relatively stable vowel and tone recognition may contribute to sentence recognition in reverberation of Mandarin-speaking CI users.

Noise Attenuation Effects on Speech Recognition of Cochlear Implant Users Inside Helicopters.

BACKGROUND: The speech recognition levels of cochlear implant (CI) users are still incompatible with ICAO hearing requirements for civil aviation pilots testing in the noisy background condition of the helicopter cockpit. In this study, we evaluated noise attenuation effects on speech recognition in the same background condition.METHODS: The study involved the evaluation of 12 Portuguese-speaking CI users with post-lingual deafness and with a pure tone average up to 35 dB HL between 500 and 2000 Hz and up to 50 dB at 3000 Hz on at least one of the ears, and of three normal hearing pilots (controls). We performed speech recognition tests using sentences, numbers, and disyllables for all participants through the VHF radio. The assessment took place inside a helicopter with engine on, using three setups: 1) with headset without the active noise cancellation; 2) activating the noise cancellation system of the headset itself; and 3) connecting the speech processor directly to the helicopter radio system.RESULTS: The headset active noise-cancellation improved only the recognition of sentences. The direct connection system compared to the headset without anti-noise attenuation significantly improved all the recognition tests. The median for numbers was 90%, but the best score for disyllables recognition was 56%.DISCUSSION: The noise attenuation resources proposed in this study improved the CI users speech recognition when exposed to the noisy helicopter cockpit. However, speech recognition of CI users still did not meet the standards of ICAO, which requires at least 80% for understanding disyllables in the speech in noise test.Caldeira JMA, Goffi-Gomez MVS, Imamura R, Bento RF. Noise attenuation effects on speech recognition of cochlear implant users inside helicopters. Aerosp Med Hum Perform. 2021; 92(11):880-885.

MAC Based Security Integration using Face Recognition in Cloud Environment

The major challenges, which come across face recognition system, are to find the age and gender in 2D/3D image of the person specifically in cloud environment. This paperis centered on face detection with MAC (Media Access Control) and biometric technology. Face scanning along with machine’s MAC address and biometric technologies has been shown to improve security controls. Face recognition can be used to search and label users and their assigned machines for sensitive purposes. Following that, it was stored in a specific database with their unique ID. In addition, the verification process has begun by comparing the models in the database. Face scanning along with speech and biometric technologies is used to improve security controls. Face recognition system may also be set up in high security machines to improve protection by allowing only registered individuals or others users. Related strategies for determining the age and gender and 2D/3D image from a specific picture are explored, as well as several modern methods for preserving protection. In this paper, the full model is explored independently with security implemented in cloud environment. The proposed model of the paper provides the integrated security features using MAC address of machine and face recognition of the machine user.

Triboelectric bending sensor based smart glove towards intuitive multi-dimensional human-machine interfaces

Recent advances in human-machine interface (HMI) lead to a renewed interest in creating intuitive and immersive interaction. Here, we designed a simple-structured and high-resolution bending angle triboelectric sensor named bending-angle triboelectric nanogenerator (BA-TENG) to construct a glove-based multi-dimensional HMI. With the assistance of a customized print circuit board (PCB), the glove-based HMI exhibits high sensitivity and low crosstalk in real-time multi-channel finger motion sensing. The signal-to-noise ratio (SNR) is improved by 19.36 dB. By systematically extracting and analyzing the multi-dimensional signal features of the BA-TENG, intuitive multi-dimensional HMIs were realized for smart-home, advanced robotic control, and a virtual keyboard with user recognition functionality. The classification accuracy of the virtual keyboard for seven users reached 93.1% by leveraging the advanced machine learning technique. The proposed BA-TENG-based smart glove reveals its potential as a solution for minimalist-design and intuitive multi-dimensional HMI, promising in diversified areas, including the Internet of things (IoT), assistive technology, and intelligent recognition systems. • Development of high bending angle resolution triboelectric nanogenerator (BA-TENG) using PDMS and silicon rubber. • The signal-to-noise ratio (SNR) is improved by 19.36 dB with the assistance of a trans-impedance amplifier. • Multi-dimensional signal features are extracted, including valley ( V ), width ( W ), hold time ( H ), and time interval ( T ). • Light-brightness control, robotic hand control, and a virtual keyboard with user identification were achieved. • Machine learning technique (SVM) is used for user identification with an accuracy of 93.1%.

Who is wearing me? TinyDL‐based user recognition in constrained personal devices

Deep learning (DL) techniques have been extensively studied to improve their precision and scalability in a vast range of applications. Recently, a new milestone has been reached driven by the emergence of the TinyDL paradigm, which enables adaptation of complex DL models generated by well-known libraries to the restrictions of constrained microcontroller-based devices. In this work, a comprehensive discussion is provided regarding this novel ecosystem, by identifying the benefits that it will bring to the wearable industry and analysing different TinyDL initiatives promoted by tech giants. The specific use case of automatic user recognition from data captured by a wearable device is also presented. The whole development process by which different DL configurations have been embedded in a real microcontroller unit is described. The attained results in terms of accuracy and resource usage confirm the validity of the proposal, which allows precise predictions in a highly constrained platform with limited input information. Therefore, this work provides insights into the viability of the integration of TinyDL models within wearables, which may be valuable for researchers, practitioners, and makers related to this industry.

Multi-modal Open World User Identification

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.

Evaluation on User Perception Effect based on Interaction Techniques in the Stereoscopic Environment

The interaction of user performance with three-dimensional (3D) objects has become an important issue in the recent development of virtual reality applications. Additionally, the basic conviction of current Virtual Reality (VR) supports the development of the viable interface between humans and machines. The research focuses on the user’s interaction technique by considering two approaches (direct and indirect interaction techniques) for the users while interacting with threedimensional objects. Numerous possible uses can benefit from virtual reality by considering a few fundamental visual and cognitive activities in the Virtual Environment (VE), such as the interpretation of space that users of clear and indirect perception are not well established. The experiment is performed in a stereoscopic environment using a reciprocal tapping task. Participants are expected to use direct pointing as well as indirect cursor techniques to select a stereoscopic spherical target. The results show that, in the sense of a direct interaction technique, user recognition of an object appears to converge in the center of a simulated area. Unfortunately, this convergence is not demonstrated in the indirect cursor situation. The pointing estimation from the users is more accurate when using the indirect interaction approach. The findings provide an understanding of the interaction characteristics done by the users in the stereoscopic environment. Importantly, developers of a virtual environment may use the result when developing effective user interface perception in specific interaction techniques.

대화형 시스템의 처리 불가능 메시지에 대한 사용자의 반응: 인간과 컴퓨터 상호작용 연구

The present research examines users’ various and sequential understanding of a non-processing message “There is no schedule that meets the condition” by a voice dialogue system. While prior HCI research on Korean voice dialogue systems focuses on recognizing users’ intentions better but pays little attention to how users understand system’s messages sequentially. The lopsided take results from a deficient orientation to user-repairs for a communication breaks down. Therefore, the paper tries to add an interpretative value on different and sequential user-repair according to their understanding of the non-processing message. The finding of 7 dialogues between the system and users shows that the users interpret its negative response differently and sequentially from non-repairable to repairable. The different interpretations result from attributing its communicative failure to a system’s poor recognition of users’ whole speech or of their certain word or phrase. These attributions are utilized through users’ distinctive and contingent choices of user-repair, leading to their employment of different strategies such as “recipient design.” The present study aims to appeal for a need of conversation analysis as a tool for analyzing human-computer interaction.

A state-of-the-art implementation of a binaural cochlear-implant sound coding strategy inspired by the medial olivocochlear reflex

Cochlear implant (CI) users find it hard and effortful to understand speech in noise with current devices. Binaural CI sound processing inspired by the contralateral medial olivocochlear (MOC) reflex (an approach termed the ‘MOC strategy’) can improve speech-in-noise recognition for CI users. All reported evaluations of this strategy, however, disregarded automatic gain control (AGC) and fine‐structure (FS) processing, two standard features in some current CI devices. To better assess the potential of implementing the MOC strategy in contemporary CIs, here, we compare intelligibility with and without MOC processing in combination with linked AGC and FS processing. Speech reception thresholds (SRTs) were compared for an FS and a MOC-FS strategy for sentences in steady and fluctuating noises, for various speech levels, in bilateral and unilateral listening modes, and for multiple spatial configurations of the speech and noise sources. Word recall scores and verbal response times in a word recognition test (two proxies for listening effort) were also compared for the two strategies in quiet and in steady noise at 5 dB signal-to-noise ratio (SNR) and the individual SRT. In steady noise, mean SRTs were always equal or better with the MOC-FS than with the standard FS strategy, both in bilateral (the mean and largest improvement across spatial configurations and speech levels were 0.8 and 2.2 dB, respectively) and unilateral listening (mean and largest improvement of 1.7 and 2.1 dB, respectively). In fluctuating noise and in bilateral listening, SRTs were equal for the two strategies. Word recall scores and verbal response times were not significantly affected by the test SNR or the processing strategy. Results show that MOC processing can be combined with linked AGC and FS processing. Compared to using FS processing alone, combined MOC-FS processing can improve speech intelligibility in noise without affecting word recall scores or verbal response times.

Visual Algorithm of VR E-Sports for Online Health Care.

The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis; FOV and frame immersion were set through typification; the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed.

Cochlear Implantation in the Setting of Menière's Disease After Labyrinthectomy: A Meta-Analysis.

Characterize the speech recognition and sound source localization of patients with unilateral Menière's disease who undergo labyrinthectomy for vertigo control with simultaneous or sequential cochlear implantation. PubMed, Embase, and Cochrane databases. The search was performed on May 6, 2020. The keywords utilized included: "Menière's disease AND cochlear implant;" "cochlear implant AND single sided deafness;" "cochlear implant AND vestibular;" and "labyrinthectomy AND cochlear implant." Manuscripts published in English with a publication date after 1995 that assessed adult subjects (≥18 years of age) were included for review. Subjects must have been diagnosed with Menière's disease unilaterally and underwent labyrinthectomy with simultaneous or sequential cochlear implantation. Reported outcomes with cochlear implant (CI) use included speech recognition as measured with the consonant-nucleus-consonant (CNC) word test and/or sound source localization reported in root-mean squared (RMS) error. The method of data collection and study type were recorded to assess level of evidence. Statistical analysis was performed with Wilcoxon signed ranks test. Data from 14 CI recipients met the criteria for inclusion. Word recognition comparisons between the preoperative interval and a postactivation interval demonstrated a significant improvement with the CI (p = 0.014), with an average improvement of 23% (range -16 to 50%). Sound source localization postoperatively with the CI demonstrated an average RMS error of 26° (SD 6.8, range 18.7-43.1°) compared to the 42° (SD 19.1, range 18-85°) in the preoperative or CI off condition, these two conditions were not statistically different (p = 0.148). Cochlear implantation and labyrinthectomy in adult patients with Menière's disease can support improvements in speech recognition and sound source localization for some CI users, though observed performance may be poorer than traditional CI candidates.

User recognition based on periocular biometrics and touch dynamics

• We proposed a multimodal biometric recognition system. • Two sources of behavioural biometric data are analyzed. • Eye movements and touch dynamics improve the performance of recognition systems. • Feature-level fusion approach. • The proposed approach allows to obtain an accuracy of over 92%. Web user behavioural recognition is the process by which web users are identified and distinguished through behavioural features. In this work, two sources of behavioural biometric data are analyzed for the development of this web user identification model, touch dynamics and the characteristics extracted from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra, Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.

Recognition Using DNN with Bacterial Foraging Optimization Using MFCC Coefficients

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.