• Journal of The Korea Institute of Healthcare Architecture
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• v.27 no.1
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• pp.17-34
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• 2021

Purpose: The purpose of this study was to find out the difference in the priority level of recognition for universal design application in public spaces by gender. Method: ANOVA(analysis of variance) and post-hoc test were conducted to determine the priority level of recognition and pattern for the disabled, non-disabled, and experts classified into males and females. Results: There was no gender difference in the comparison by sector for all males and females. However, in comparing of domains and facilities, women showed a higher level of recognition than men in the building sector and cross domain. When comparing space consumers and producers by dividing them into male and female groups, women showed a higher level of recognition than men in producers, but there was no gender difference between consumers. In comparison by sector, domain and facility, women producers also showed a higher level of recognition in the road sector, park and recreation sector, sidewalk domain, four-spaces in the park and recreation sector, and six-spaces in the building sector than men producers. Also, in the building sector, women producers and consumers showed a higher recognition level than men. Comparing the disabled, non-disabled people and experts by dividing them into male and female groups, in the case of non-disabled people and experts, women showed a higher level of recognition than men, while men showed a higher level of recognition than women in the disabled. In addition, there were differences in recognition patterns in many spaces and facilities by gender. Implications: This study is meaningful in comparing the differences in the priority level of recognition and patterns between men and women to apply universal design for people of all ages and both sexes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.568-574
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• 2010

In an effort to improve the speech recognition rate, we investigated performance comparison between speaker-independent and gender-specific speech recognitions. For this purpose, 20 male and 20 female speakers each pronounced 300 isolated Korean words and the speeches were divided into 4 groups: female, male, and two mixed genders. To examine the validity for the gender-specific speech recognition, Fourier spectrum and MFCC feature vectors averaged over male and female speakers separately were examined. The result showed distinction between the two genders, which supports the motivation for the gender-specific speech recognition. In experiments of speech recognition rate, the error rate for the gender-specific case was shown to be less than50% compared to that of the speaker-independent case. From the obtained results, it might be suggested that hierarchical recognition of gender and speech recognition might yield better performance over the current method of speech recognition.

• The Journal of Korean Medicine
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• v.39 no.2
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• pp.80-91
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• 2018

Objectives: The purpose of this study was to investigate stress and health recognition in Korean medical student by gender. Methods: 393 Korean medical student participated in this study. They were surveyed demographic characteristics(gender, age, drinking, morning diet, health recognition and etc), medical stress scale, Maslachh burnout inventory, Korea acceptance and action, and Ego Resilience Scale. For statistical analysis, chi-square test, Student t-test, correlation analysis, and simple regression analysis has been used. Results: In Medical stress scale and ego resilience, it is not significant difference by gender. Female students were significantly higher than male students in Maslach burnout inventory(P=0.015). Male students were significantly higher than female students in Korea Acceptance and Action(P=0.002). Health recognition have negative correlation between medical stress and academic exhaustion, and positive correlation between psychological flexibility and ego resilience. Conclusions: Through these results, we need to further study and develop strategies to reduce stress according to gender in Korean medical college.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4510-4526
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• 2017

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

• Korean Journal of Social Welfare
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• v.57 no.3
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• pp.31-50
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• 2005

The perspectives on the gender equality are various. Therefore the relationship between social policy and gender equality must be thought in the sense of the following question: What kind of gender equality pursue social policy? In spite of the mentioned question the mainstreaming discussion about the relationship between social policy and gender equality focuses on how a social policy leads to gender inequality. The debate about the perspective with which social policy pursues to conglomerate or abolish gender inequality is not popular. In this study the Korean National Pension System is analysed according to the gender neutral approach, the gender recognition approach, the gender reconstruction approach and the gender reinforcement approach. As a result, the Korean National Pension System has a character as a gender neutral approach. Therefore the critics on the National Pension System should focus on this perspective. In the future, when the pension credit system, the guarantee of right to pension in the case of divorce and the basic pension guarantee would be introduced, the Korean National Pension System could be featured as a field of social policy that has a gender recognition approach.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.307-316
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• 2012

Two experiments were conducted to test effects of ethnicity and gender of face stimuli and lineup procedure on face recognition. In experiment 1, all stimuli were frontal male pictures of Southeast Asian and Northeast Asian. It was tested that whether accuracy of face recognition improve when ethnicity of participant and stimulus was identical (own-ethnicity advantage). In experiment 2, the 'own-gender advantage' was tested using images of Northeast Asian's frontal male and female faces. All participants were Northeast Asian. The results showed that the own-ethnicity advantage or the effect of line-up procedure was not found, but accuracy of face recognition was high when target face was Southeast Asian. Interestingly, the own-gender advantages was observed in only case of female participants. It was discussed that face recognition could be affected by attention.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1370-1376
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• 2012

This paper proposes a gender recognition algorithm which classifies a male or female speaker. In this paper, characteristic vectors for the male and female speaker are analyzed, and recognition experiments for the proposed gender recognition by a neural network are performed using these characteristic vectors for the male and female. Input characteristic vectors of the proposed neural network are 10 LPC (Linear Predictive Coding) cepstrum coefficients, 12 LPC cepstrum coefficients, 12 FFT (Fast Fourier Transform) cepstrum coefficients and 1 RMS (Root Mean Square), and 12 LPC cepstrum coefficients and 8 FFT spectrum. The proposed neural network trained by 20-20-2 network are especially used in this experiment, using 12 LPC cepstrum coefficients and 8 FFT spectrum. From the experiment results, the average recognition rates obtained by the gender recognition algorithm is 99.8% for the male speaker and 96.5% for the female speaker.

• Journal of Korean Biological Nursing Science
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• v.12 no.3
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• pp.166-174
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• 2010

Purpose: The purpose of this study was to compare the cardiac knowledge and symptoms recognition between men and women with acute coronary syndrome (ACS). Methods: Cross-sectional survey research design and convenience sampling were used in this study. 64 men and 42 women from a university medical center were participated in this study. Data collection were used with self reported questionnaires and medical records. Results: There was no difference in cardiac knowledge by gender. Women marked the higher score in symptoms recognition than men. Significant gender differences were observed in the reports of several symptoms (headache, nausea, palpitation, hand paresthesia, and leg numbness) and with ACS. There was significant correlation between cardiac knowledge and symptom recognition on both men and women. Conclusion: These findings suggest that health care professionals should pay attention to the differences in clinical symptoms between women and men. Furthermore, tailored information about possible symptoms of coronary artery disease according to the patient's gender is needed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.779-782
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• 2017

OpenBR is a framework for researching new facial recognition methods, improving existing algorithms, interacting with commercial systems, measuring perceived performance, and deploying automated biometric systems. Designed to facilitate rapid algorithm prototyping, it features a mature core framework, flexible plug-in system, and open and closed source development support. The established algorithms can be used for specific forms such as face recognition, age estimation, and gender estimation. In this paper, we describe the framework of OpenBR and implement facial recognition, gender estimation, and age estimation using supported programs.