Gender and Age Classification Based on Human Features to Detect Illicit Activity in Suspicious Sites

Torres, Edgar P.; Granizo, Sergio L.; Hernández-Álvarez, Myriam

doi:10.1109/csci49370.2019.00081

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Estimating human facial age may be a problem of multi-classification [54][55][56], regression [57,58], or a hybrid of both techniques [18,59]. Many results of previous research explorations have proved that a hybrid or hierarchical age estimation approach outperformed single-stage approaches [60].…”

Section: Hierarchical Classification and Regressionmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.

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Section: Hierarchical Classification and Regressionmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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“…Also, in [28], the authors used age classification for risk stratification in glioma patients. In [29], an age classification system was used for detecting illicit activity in suspicious sites.…”

Section: Related Workmentioning

confidence: 99%

A Multi-task Deep Learning System for Face Detection and Age Group Classification for Masked Faces

Yolcu

Öztel

2021

Sakarya University Journal of Science

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COVID-19 is an ongoing pandemic, and according to experts, using a face mask can reduce the spread of the disease. On the other hand, masks cause occlusion in faces and can create safety problems such as the recognition of the face and the estimation of its age. To prevent the spread of COVID-19, some countries have restrictions according to age groups. Also, in different countries, people in some age groups have safety restrictions such as driving and consuming alcohol, etc. But these rules are difficult to follow due to occlusion in faces. Automated systems can assist in monitoring these rules. In this study, a deep learning-based automated multi-task face detection and age group classification system is proposed for masked faces. The system first detects masked/no-masked-faces. Then, it classifies them according to age-groups. It works for multi-person regardless of indoor/outdoor environment. The system achieved 92.0% average precision score for masked face detection using YOLO with resnet50 network. Also, 83.87% accuracy for classifying age groups with masked faces and 84.48% accuracy for nomasked faces using densenet201 network have been observed. It produced better results compared to the literature. The results are significant because they show that a reliable age classification for masked faces is possible.

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“…One of the face detection modeling is using Convolutional Neural Network (CNN) modeling. CNN is one of the neural networks used in image data (Torres, Granizo, and Hernandez-Alvarez, 2019). CNN has the ability to detect objects in an image.…”

Section: Introductionmentioning

confidence: 99%

Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Listio¹

2022

SinkrOn

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Convolutional neural network (CNN) is one of the neural networks used in image data. CNN has a good ability to detect objects in an image. This study discusses the comparison of two deep learning models based on convolutional neural network, namely the Inception-V3 method and the MobileNet method. Both algorithms are analyzed fairly on gender classification using eye images. There have been many research completions that have conducted studies on gender classification based on faces, but gender classification based on eyes has many challenges. This gender classification is grouped into two classes, namely male and female. This study aims to build a gender classification model from eye image. The processes in this research include selecting the dataset, preprocessing the data, dividing the data which is divided into training data and test data, modeling, and evaluating the performance of the model. This study uses a public dataset, where the data contains a total of 2,681 images consisting of 1251 male eyes and 1430 female eyes. This study concludes that gender classification using eye image using the Inception-V3 method is better than the MobileNet method. This is obtained based on the accuracy value generated by the Inception-V3 method which is higher than the MobileNet-V2 method which obtains an accuracy of 91.82%.

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Gender and Age Classification Based on Human Features to Detect Illicit Activity in Suspicious Sites

Cited by 8 publications

References 6 publications

Semantic Human Face Analysis for Multi-level Age Estimation

Semantic Human Face Analysis for Multi-level Age Estimation

A Multi-task Deep Learning System for Face Detection and Age Group Classification for Masked Faces

Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

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