CNN Based Features Extraction for Age Estimation and Gender Classification

Benkaddour, Mohammed Kamel

doi:10.31449/inf.v45i5.3262

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…The sigmoid activation function was used in the convolutional layer [15]. The pooling layer used to maxpool.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…In this paper, the density peak clustering algorithm used for color extraction does not require a prior determination of the number of clustering types and clustering centers. First, the image is converted into the color space of CIE Lab, and then the local density and relative distance of every pixel are calculated: ④ The texture and color extracted in steps ② and ③ are input into the CNN model [15] for calculation. The basic structure of the CNN model includes the input layer, convolutional layer, pooling layer, and output layer.…”

Section: Clothing Design Evaluation Model Combined With Image Process...mentioning

confidence: 99%

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Evaluation of innovative design of clothing image elements using image processing

Nie¹

2022

IJCAI

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Excellent design of clothing image elements can enhance the attractiveness of clothing to customers. This paper used image processing technology to extract texture and color features for innovative designs of clothing image elements and used the convolutional neural network (CNN) model to evaluate some designs. The CNN model was compared with the back-propagation neural network (BPNN) model in the example analysis, and three designs were evaluated. The results showed that the image processing-based CNN model had good evaluation performance and obtained evaluation results closer to the manual evaluation results than the BPNN model. The evaluation results of the three designs also showed that all three designs achieved innovative design through effectively combining multiple image elements.Povzetek: V članku je opisan nov način snovanja oblačil s pomočjo konvolucijske nevronske mreže.

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“…The sigmoid activation function was used in the convolutional layer [15]. The pooling layer used to maxpool.…”

Section: Analysis Methodsmentioning

confidence: 99%

Section: Clothing Design Evaluation Model Combined With Image Process...mentioning

confidence: 99%

Evaluation of innovative design of clothing image elements using image processing

Nie¹

2022

IJCAI

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show abstract

“…У дослідженні [6] показано, що для оцінки віку та гендерної класифікації можна отримати значне покращення, вивчаючи уявлення з використанням глибоких згорткових нейронних мереж (CNN). Метод нейронної мережі з прямим зв'язком, використаний у цьому дослідженні, підвищує надійність для дуже варіабельних завдань розпізнавання без обмежень для визначення статі та вікової групи.…”

Section: аналіз досліджень та публікаційunclassified

Creation of Age Estimation System

Kryvenchuk¹,

YAKYMIV²

2022

Herald KhmNU.TS

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We are surrounded by different transmitting and processing information devices on a daily basis. Analysis of certain selected characteristics of each person allows humanity to achieve previously unthinkable technological development. In particular, the human face. We can predict a person’s age, determine his mood or identify someone in the crowd using modern algorithms. As a “window to the soul”, the human face provides important information related to its individual traits. With the help of this information, a person can determine such characteristics as ethnic origin, gender, age, and emotions, on the basis of which he is able to take appropriate action, which plays a significant role in non-verbal communication between people. Age estimation is useful in applications where you do not need to specifically identify a person, but we want to know (approximate) their age. With the help of the age approximation system, it is possible to ensure partial digitalization of security control and monitoring systems. With the help of a monitoring camera, the age estiomation system can identify minors and prevent them from entering places where they are not allowed; not to allow minors to buy tobacco products or alcohol from vending machines; to warn the elderly person of possible danger. Age assessment software can also be used in healthcare systems. The authors personally are interested in approximating human age based on images to personalize human-machine interaction. The paper has the description and the results of the creation of an age estimation system, the basis of which is a convolutional neural network. The user can connect with the system through a simple interface which allows you to upload the image for analysis or turn on the camera to get age estimates in real-time. The practical value of this work is the developed high-quality system which is ready for use and implementation in the relevant fields.

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“…Figure 3 shows that the CapsNet optimized recognition algorithm had a high convergence speed. The CapsNet optimized recognition algorithm was compared with other algorithms, including Random Forest (RF) [12], C4.5 [13], Support Vector Machine (SVM) [14], and Convolutional Neural Network (CNN) [15], and the results are shown in Figure 4.…”

Section: Experimental Analysismentioning

confidence: 99%

An optimized recognition algorithm for SSL VPN protocol encrypted traffic

Xian

2021

IJCAI

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CNN Based Features Extraction for Age Estimation and Gender Classification

Cited by 22 publications

References 16 publications

Evaluation of innovative design of clothing image elements using image processing

Evaluation of innovative design of clothing image elements using image processing

Creation of Age Estimation System

An optimized recognition algorithm for SSL VPN protocol encrypted traffic

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