• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.430-435
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• 2006

In this paper, we propose a method based on multimodal biometrics system using the face and signature under ubiquitous computing environments. First, the face and signature images are obtained by PDA and then these images with user ID and name are transmitted via WLAN(Wireless LAN) to the server and finally the PDA receives verification result from the server. The multimodal biometrics recognition system consists of two parts. In client part located in PDA, user interface program executes the user registration and verification process. The server consisting of the PCA and LDA algorithm shows excellent face recognition performance and the signature recognition method based on the Kernel PCA and LDA algorithm for signature image projected to vertical and horizontal axes by grid partition method. The proposed algorithm is evaluated with several face and signature images and shows better recognition and verification results than previous unimodal biometrics recognition techniques.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.757-758
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• 2008

This paper aims to propose an emergency recognition system using multimodal information extracted by an image processing module, a voice processing module, and a gravity sensor processing module. Each processing module detects predefined events such as moving, stopping, fainting, and transfer them to the multimodal integration module. Multimodal integration module recognizes emergency situation by using the transferred events and rechecks it by asking the user some question and recognizing the answer. The experiment was conducted for a faint motion in the living room and bathroom. The results of the experiment show that the proposed system is robust than previous methods and effectively recognizes emergency situations at various situations.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.231-234
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• 2011

Audiovisual-based human emotion recognition can be considered a good approach for multimodal humancomputer interaction. However, the optimal multimodal information fusion remains challenges. In order to overcome the limitations and bring robustness to the interface, we propose a framework of automatic human emotion recognition system from speech and face display. In this paper, we develop a new approach for fusing information in model-level based on the relationship between speech and face expression to detect automatic temporal segments and perform multimodal information fusion.

• Journal of the Ergonomics Society of Korea
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• v.25 no.2
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• pp.135-146
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• 2006

Multimodal interfaces are recognition-based technologies that interpret and encode hand gestures, eye-gaze, movement pattern, speech, physical location and other natural human behaviors. Modality is the type of communication channel used for interaction. It also covers the way an idea is expressed or perceived, or the manner in which an action is performed. Multimodal Interfaces are the technologies that constitute multimodal interaction processes which occur consciously or unconsciously while communicating between human and computer. So input/output forms of multimodal interfaces assume different aspects from existing ones. Moreover, different people show different cognitive styles and individual preferences play a role in the selection of one input mode over another. Therefore to develop an effective design of multimodal user interfaces, input/output structure need to be formulated through the research of human cognition. This paper analyzes the characteristics of each human modality and suggests combination types of modalities, dual-coding for formulating multimodal interaction. Then it designs multimodal language and input synchronization method according to the granularity of input synchronization. To effectively guide the development of next-generation multimodal interfaces, substantially cognitive modeling will be needed to understand the temporal and semantic relations between different modalities, their joint functionality, and their overall potential for supporting computation in different forms. This paper is expected that it can show multimodal interface designers how to organize and integrate human input modalities while interacting with multimodal interfaces.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.193-201
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• 2020

The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

• Speech Sciences
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• v.8 no.3
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• pp.123-132
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• 2001

In recent years research on HCI technology has been very active and speech recognition is being used as its typical method. Its recognition, however, is deteriorated with the increase of surrounding noise. To solve this problem, studies concerning the multimodal HCI are being briskly made. This paper describes automated lipreading for bimodal speech recognition on the basis of image- and speech information. It employs audio-visual DB containing 1,074 words from 70 voice and tri-viseme as a recognition unit, and state tied HMM as a recognition model. Performance of automated recognition of 22 to 1,000 words are evaluated to achieve word recognition of 60.5% in terms of 22word recognizer.

• ETRI Journal
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• v.46 no.1
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• pp.71-81
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• 2024

Recent advances in deep learning for speech and visual recognition have accelerated the development of multimodal speech recognition, yielding many innovative results. We introduce a Korean audiovisual speech recognition corpus. This dataset comprises approximately 150 h of manually transcribed and annotated audiovisual data supplemented with additional 2000 h of untranscribed videos collected from YouTube under the Creative Commons License. The dataset is intended to be freely accessible for unrestricted research purposes. Along with the corpus, we propose an open-source framework for automatic speech recognition (ASR) and audiovisual speech recognition (AVSR). We validate the effectiveness of the corpus with evaluations using state-of-the-art ASR and AVSR techniques, capitalizing on both pretrained models and fine-tuning processes. After fine-tuning, ASR and AVSR achieve character error rates of 11.1% and 18.9%, respectively. This error difference highlights the need for improvement in AVSR techniques. We expect that our corpus will be an instrumental resource to support improvements in AVSR.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.743-748
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• 2007

Recently, various types of camera mouse are developed using the image processing. The camera mouse showed limited performance compared to the traditional optical mouse in terms of the response time and the usability. These problems are caused by the mismatch between the size of the monitor and that of the active pixel area of the CMOS Image Sensor. To overcome these limitations, we designed a new input device that uses the face recognition as well as the speech recognition simultaneously. In the proposed system, the area of the monitor is partitioned into 'n' zones. The face recognition is performed using the web-camera, so that the mouse pointer follows the movement of the face of the user in a particular zone. The user can switch the zone by speaking the name of the zone. The multimodal mouse is analyzed using the Keystroke Level Model and the initial experiments was performed to evaluate the feasibility and the performance of the proposed system.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.302-308
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• 2013

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).