• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.606-611
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• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.19-25
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• 2020

This study examined thirty-one Korean students' pronunciation of an English passage using a speech recognition application, Speechnotes, and two Canadian raters' evaluations of their speech according to the International English Language Testing System (IELTS) band criteria to assess the possibility of using the application as a teaching aid for pronunciation education. The results showed that the grand average percentage of correctly recognized words was 77.7%. From the moderate recognition rate, the pronunciation level of the participants was construed as intermediate and higher. The recognition rate varied depending on the composition of the content words and the function words in each given sentence. Frequency counts of unrecognized words by group level and word type revealed the typical pronunciation problems of the participants, including fricatives and nasals. The IELTS bands chosen by the two native raters for the rainbow passage had a moderately high correlation with each other. A moderate correlation was reported between the number of correctly recognized content words and the raters' bands, while an almost a negligible correlation was found between the function words and the raters' bands. From these results, the author concludes that the speech recognition application could constitute a partial aid for diagnosing each individual's or the group's pronunciation problems, but further studies are still needed to match human raters.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.266-270
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• 2006

In this paper, we propose the human identification system based on Hidden Markov model using gait. Since each gait cycle consists of a set of continuous motion states and transition across states has probabilistic dependences, individual gait can be modeled using Hidden Markov model. We assume that individual gait consists of N discrete transitions and we propose gait feature representation, Modified Motion Silhouette Image (MMSI) to represent and recognize individual gait. MMSI is defined as a gray-level image and it provides not only spatial information but also temporal information. The experimental results show gait recognition performance of proposed system.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• International Journal of Highway Engineering
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• v.12 no.1
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• pp.9-20
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• 2010

This study was intended to identify, among various characteristics of tunnel, the relationship between the design factors comprising the driver's psychological stability, easiness and the sensitivity and then to suggest the mechanism for evaluating the tunnel view, and to that end, the study attempted to evaluate the relations between the physical elements comprising the tunnel shape and the variation of driver's emotional recognition, thereby proposing the measures to create the scenic environment. As a result of LISREL modeling to identify the characteristics of emotional recognition to tunnel view, the elements affecting tunnel view appeared to be emotional image created by the combination of elements comprising the tunnel view. Such emotional image can be explained by design elements and individual characteristics, and the effect of design element appeared to be greater than individual characteristics. The relations between individual characteristics and design element appeared to be positive (+) and the relations between the "safety" and "variability" was significant. And the "safety" have had greater effect on view recognition than "variability", indicating that the drivers tend to give more importance to "safety", but also require the "variability"on the other hand.

• Journal of the Optical Society of Korea
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• v.17 no.4
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• pp.328-335
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• 2013

Due to the rapid advancement of auto-refractor technology, most optometry shops provide refraction services. Despite their speed and convenience, the measurement values provided by auto-refractors include a significant degree of error due to psychological and physical factors. Therefore, there is a need for repetitive testing to obtain a smaller mean error value. However, even repetitive testing itself might not be sufficient to ensure accurate measurements. Therefore, research on a method of measurement that can complement auto-refractor measurements and provide confirmation of refraction results needs to be conducted. The customized optometry model described herein can satisfy the above requirements. With existing technologies, using human eye measurement devices to obtain relevant individual optical feature parameters is no longer difficult, and these parameters allow us to construct an optometry model for individual eyeballs. They also allow us to compute visual images produced from the optometry model using the CODE V macro programming language before recognizing the diffraction effects visual images with the neural network algorithm to obtain the accurate refractive diopter. This study attempts to combine the optometry model with the back-propagation neural network and achieve a double check recognition effect by complementing the auto-refractor. Results show that the accuracy achieved was above 98% and that this application could significantly enhance the service quality of refraction.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.742-749
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• 2020

This paper proposes a method for recognizing both phoneme and emotion using a common neural network and a multi-task training method for the common neural network. The common neural network performs the same function for both recognition tasks, which corresponds to the structure of multi-information recognition of human using a single auditory system. The multi-task training conducts a feature modeling that is commonly applicable to multiple information and provides generalized training, which enables to improve the performance by reducing an overfitting occurred in the conventional individual training for each information. A method for increasing phoneme recognition performance is also proposed that applies weight to the phoneme in the multi-task training. When using the same feature vector and neural network, it is confirmed that the proposed common neural network with multi-task training provides higher performance than the individual one trained for each task.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.57-62
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• 2010

Recently in the mobile market, the communication technology which bases on the sense of sight, sound, and touch has been developed. However, human beings uses all five - vision, auditory, palatory, olfactory, and tactile - senses to communicate. Therefore, the current paper presents a technology which enables individuals to be aware of other people's emotions through a machinery device. This is achieved by the machine perceiving the tone of the voice, body temperature, pulse, and other biometric signals to recognize the emotion the dispatching individual is experiencing. Once the emotion is recognized, a scent is emitted to the receiving individual. A system which coordinates the emission of scent according to emotional changes is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2067-2073
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• 2001

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify a person. They have sensors to pick up some physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However, lip-print recognition has been less developed than recognition of other human physical attributes such as the fingerprint, voice patterns, retinal at blood vessel patterns, or the face. The lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from the pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. The multi-resolution architecture allows us to reduce the false recognition rate from 15% to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.