• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1334-1337
• /
• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1034-1038
• /
• 2005

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5605-5623
• /
• 2016

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.

• Human Ecology Research
• /
• v.51 no.4
• /
• pp.413-424
• /
• 2013

The purpose of this study is to examine the influence of social face sensitivity on vanity and consumption behavior. Questionnaires were administered to 461 college students living in Deagu Metropolitan City and Kyungbook province. Frequency, factor analysis, reliability analysis, regression analysis, and t-test were used for data analysis. Social face sensitivity was categorized into consciousness of being embarrassed, social formality, other consciousness and prestigiousness. Vanity factors were found as physical price, achievement concern, achievement price, and physical concern. Consumption behavior were categorized into 5 factors such as ostentatious consumption, impulsive buying, external information search, brand trust, utilization of internet information and material-oriented. Consciousness of being embarrassed, social formality, and other consciousness, the sub-variables of social face sensitivity had significant effects on achievement concerns and physical concerns. Other consciousness of social face sensitivity had significant effects on all factors of consumption behaviors. There was significant differences in the prestige of social face sensitivity and physical concerns of vanity and ostentatious consumption, impulsive buying, and also in external information search and utilization of internet information of consumer behavior. This indicates that women showed high physical concerns for vanity and ostentatious consumption, impulsive buying, external information search and utilization of internet information of consumption behavior while men care more about prestigiousness of social face sensitivity.

• International journal of advanced smart convergence
• /
• v.11 no.3
• /
• pp.64-71
• /
• 2022

Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.707-720
• /
• 2014

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

• International Journal of Knowledge Content Development & Technology
• /
• v.6 no.2
• /
• pp.29-42
• /
• 2016

Performing an online health search is a popular activity conducted on the Internet. Research studies from developed countries provide information on source used, type of search performed and devices used to perform the search. However, the same cannot be said about the online health information searching scene in South East Asia. Online health information searching is gaining popularity in South East Asia. Citizens in these countries are turning to the Internet to obtain health information quickly. Current research studies pertaining to online health information searching in South East Asian is limited, particularly relating to search experiences of South East Asian health searchers. Search experience is pertinent as it could deter or encourage the possibility of conducting future health searches. In this research study, a user study was conducted to describe the online search experience of South East Asian health searchers. A face to face interview with 50 participants was conducted. The interview was audio recorded and transcribed verbatim. Results indicate participants have positive and negative search experiences. In some cases, post search outcomes influenced the search experience. Results of this research study contribute to the growing domain of knowledge in relation to online health information searching. Results of this study also provide an understanding pertaining to the search experience of South East Asian online health searchers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.417-417
• /
• 2000

This paper proposes a face detection and recognition method that combines the template matching method and the eigenface method with the neural network. In the face extraction step, the skin color information is used. Therefore, the search region is reduced. The global property of the face is achieved by the eigenface method. Face recognition is performed by a neural network that can learn the face property.

• The Journal of Korea Robotics Society
• /
• v.4 no.4
• /
• pp.257-264
• /
• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.49-52
• /
• 2002

This paper presents a fast algorithm for face detection from color images on internet. We use Mahalanobis distance between standard skin color and actual pixel color on IQ color space to segment skin color regions. The skin color regions are the candidate face region. Further, the locations of eyes and mouth regions are found by computing average pixel values on horizontal and vertical pixel lines. The geometry of mouth and eye locations is compared to the standard face geometry to eliminate false face regions. Our Method is simple and fast so that it can be applied to face search engine for internet.