• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1323-1329
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on the dynamic hand movement are used together. In this paper, we propose a hierarchical hand pose model based on finger position and shape for hand expression recognition. For hand pose recognition, a finger model representing the finger state and a hand pose model using the finger state are hierarchically constructed, which is based on the open source MediaPipe. The finger model is also hierarchically constructed using the bending of one finger and the touch of two fingers. The proposed model can be used for various applications of transmitting information through hands, and its usefulness was verified by applying it to number recognition in sign language. The proposed model is expected to have various applications in the user interface of computers other than sign language recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.272-287
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• 2017

This paper proposes new methods, named Derivative Code (DerivativeCode) and Derivative Code Pattern (DCP), for object recognition. The discriminative derivative code is used to capture the local relationship in the input image by concatenating binary results of the mathematical derivative value. Gabor based DerivativeCode is directly used to solve the palmprint recognition problem, which achieves a much better performance than the state-of-art results on the PolyU palmprint database. A new local pattern method, named Derivative Code Pattern (DCP), is further introduced to calculate the local pattern feature based on Dervativecode for object recognition. Similar to local binary pattern (LBP), DCP can be further combined with Gabor features and modeled by spatial histogram. To evaluate the performance of DCP and Gabor-DCP, we test them on the FERET and PolyU infrared face databases, and experimental results show that the proposed method achieves a better result than LBP and some state-of-the-arts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• Annual Conference of KIPS
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1465-1473
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• 2006

In this paper, we present an approach for facial expression recognition using Active Shape Models(ASM) and a state-based model in image sequences. Given an image frame, we use ASM to obtain the shape parameter vector of the model while we locate facial feature points. Then, we can obtain the shape parameter vector set for all the frames of an image sequence. This vector set is converted into a state vector which is one of the three states by the state-based model. In the classification step, we use the k-NN with the proposed similarity measure that is motivated on the observation that the variation-regions of an expression sequence are different from those of other expression sequences. In the experiment with the public database KCFD, we demonstrate that the proposed measure slightly outperforms the binary measure in which the recognition performance of the k-NN with the proposed measure and the existing binary measure show 89.1% and 86.2% respectively when k is 1.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.55-70
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• 2009

Vision-based Human computer interaction is an emerging field of science and industry to provide natural way to communicate with human and computer. In that sense, inferring the emotional state of the person based on the facial expression recognition is an important issue. In this paper, we present a novel approach to recognize facial expression from a sequence of input images using emotional specific HMM (Hidden Markov Model) and facial motion tracking based on optical flow. Conventionally, in the HMM which consists of basic emotional states, it is considered natural that transitions between emotions are imposed to pass through neutral state. However, in this work we propose an enhanced transition framework model which consists of transitions between each emotional state without passing through neutral state in addition to a traditional transition model. For the localization of facial features from video sequence we exploit template matching and optical flow. The facial feature displacements traced by the optical flow are used for input parameters to HMM for facial expression recognition. From the experiment, we can prove that the proposed framework can effectively recognize the facial expression in real time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.1-12
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• 2016

Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.3-10
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• 1999

This paper describes the development and evaluation of a Korean address input system employing automatic speech recognition technique as user interface for input Korean address. Address consists of cities, provinces and counties. The system works on a window 95 environment of personal computer with built-in soundcard. In the speech recognition part, the Continuous density Hidden Markov Model(CHMM) for making phoneme like units(PLUs) and One Pass Dynamic Programming(OPDP) algorithm is used for recognition. For address recognition, Finite State Automata(FSA) suitable for Korean address structure is constructed. To achieve an acceptable performance against the variation of speakers, microphones, and environmental noises, Maximum a posteriori(MAP) estimation is implemented in adaptation. And to improve the recognition speed, fast search method using variable pruning threshold is newly proposed. In the evaluation tests conducted for the 100 connected words uttered by 3 males the system showed above average 96.0% of recognition accuracy for connected words after adaption and recognition speed within 2 seconds, showing the effectiveness of the system.