• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• Genomics & Informatics
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• v.15 no.4
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• Journal of Oral Medicine and Pain
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• v.24 no.1
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• pp.95-106
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• 1999

This study was performed to investigate the age distribution with tooth calcification and degree of eruption of permanent teeth. For the study, healthy 184 patients from 5 to 19 years old without any previous serious dental treatment were randomly selected, and intraoral standard films and dental casts were taken for evaluation of stage of calcification and degree of eruption, respectively. Tooth calcification of 13 stages, designed by the author based on the Nolla's classification and eruption level of 4 or 5 degree was used. Data were processed by SAS/Stat program and the obtained results were as follows; 1. The age of root completed with open apex in lower posterior teeth were 13.8 years for first premolar, 14.0 years for second premolar, 10.5 years for first molar, and 14.2 years for second molar. There were no significant difference between right and left side. 2. As for the sequence of eruption, first molar was the first teeth erupted in upper arch, while central incisor was the first teeth in lower arch. In general, eruption of lower teeth were slightly earlier than the corresponding teeth of upper arch. 3. There were no difference of age of the same stage of development between Nolla's and the author's classification. From the results, the author's classification can be used for estimation of age with more finely in age of 8 to 15 years old. 4. Multiple regression equations for age with Nolla's(Ns) and the author's(Ks) classification of tooth calcification, and degree of eruption(DE) were as follow; Age(by #34) = 7.55 + 0.76Ks34 + 0.80DE34 - 0.72Ns34 Age(by #35) = 7.10 + 0.81Ks35 + 0.6IDE35 Age(by #37) = 6.61 + 0.82Ks37 + 0.5IDE37. Age(by #44) = 7.02 + 0.62Ks44 + 0.82DE44 Age(by #45) = 8.04 + 0.93Ks45 + 0.64DE45 - 0.89Ns45 Age(by #47) = 6.40 + 0.86Ks47 + 0.56DE47.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.151-157
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• 2012

Recently, owing to the technology advances in computer science and image processing, age of face classification have become prevalent topics. It is difficult to estimate age of facial shape with statistical figures because facial shape of the person should change due to not only biological gene but also personal habits. In this paper, we proposed a robust age of face classification method by using Gabor feature and fuzzy support vector machine(SVM). Gabor wavelet function is used for extracting facial feature vector and in order to solve the intrinsic age ambiguity problem, a fuzzy support vector machine(FSVM) is introduced. By utilizing the FSVM age membership functions is defined. Some experiments have conducted to testify the proposed approach and experimental results showed that the proposed method can achieve better age of face classification precision.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• Korean Journal of Child Studies
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• v.20 no.3
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• pp.259-273
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• 1999

This studied investigated children's music identification, classification, and seriation cognitive task performance abilities by age and sex. The subjects were l20 six-, eight-, and ten-year-old school children. There were significant positive correlations among music cognition tasks and significant age and sex differences within each of the music tasks. Ten-year-old children were more likely to complete their music identification tasks than the younger children and girls were more likely than boys to complete their music identification tasks. Eight- and 10-year-old children were more likely to complete their music classification tasks than the younger group. Piagetian stage theory was demonstrated in children's music classification task performance. There was an age-related increase in the performance of the music seriation tasks. Developmental sequential theory was demonstrated in music seriation performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.568-570
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• 2023

The problem of classifying of age, gender, and race images still poses challenges. Despite deep and machine learning strides, convolutional neural networks (CNNs) remain pivotal in addressing these issues. This paper introduces a novel CNN-based approach for accurate and efficient age, gender, and race classification. Leveraging CNNs with residual blocks, our method enhances learning while minimizing computational complexity. The model effectively captures low-level and high-level features, yielding improved classification accuracy. Evaluation of the diverse 'fair face' dataset shows our model achieving 56.3%, 94.6%, and 58.4% accuracy for age, gender, and race, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.69-75
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• 2014

Existing People Counting System using a single overhead mounted camera has limitation in object recognition and counting in various environments. Those limitations are attributable to overlapping, occlusion and external factors, such as over-sized belongings and dramatic light change. Thus, this paper proposes the new concept of People Counting System by Facial Age Group using two depth cameras, at overhead and frontal viewpoints, in order to improve object recognition accuracy and robust people counting to external factors. The proposed system is counting the pedestrians by five process such as overhead image processing, frontal image processing, identical object recognition, facial age group classification and in-coming/out-going counting. The proposed system developed by C++, OpenCV and Kinect SDK, and it target group of 40 people(10 people by each age group) was setup for People Counting and Facial Age Group classification performance evaluation. The experimental results indicated approximately 98% accuracy in People Counting and 74.23% accuracy in the Facial Age Group classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2480-2495
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• 2014

This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

• Journal of Korean Foot and Ankle Society
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• v.10 no.2
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• pp.196-200
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• 2006

Purpose: To study prognostic factors of unilateral calcaneus fracture underwent surgery. Materials and Methods: We selected appropriate 60 cases of 236 calcaneus fracture cases between March 1985 and March 2004, and analyzed the correlation between sex, age, smoking, injury mechanism, Essex-Lopresti classification of calcaneus fracture, preoperative Bohler angle, postoperative Bohler angle, postoperative 1 year Bohler angle and Visual Analogue Scale (VAS), P.S. Kerr's Calcaneal Fracture Score (CFSS). The average age was 41.4 and average follow up period was 74 (12 to 240) months. Results: For follow up period, average VAS is 3.43 and CFSS is 81.23. The sex, age, smoking, injury mechanism, and preoperative, postoperative, postoperative 1 year Bohler angle had no correlation with the prognosis. But the Essex-Lopresti classification of calcaneus fracture, tongue type had better prognosis than joint depression type (VAS : p=0.041, CFSS : p=0.021). Conclusion: In unilateral calcaneus fracture, the sex, age, smoking, injury mechanism, preoperative Bohler angle, postoperative Bohler angle, postoperative 1 year Bohler angle had no correlation with the prognosis of fracture, but in Essex-Lopresti classification, the tongue type fracture had better prognosis than the joint depression type.