Gender Recognition Through Face Using Deep Learning

Dhomne, Amit; Kumar, R.; Bhan, Vijay

doi:10.1016/j.procs.2018.05.053

Cited by 70 publications

(34 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…they have achieved 98% success in gender recognition with their CNN-based models. Dhomne et al [18] have proposed a VGGNet model based on D-CNN using facial images for gender recognition. Xu et al [19] have been proposing Hierarchical Multi-task Network (HMTNet), a deep neural network that can identify both sex, race, and facial beauty from a person's portrait image.…”

Section: Figure 1 Face Recognition Timelinementioning

confidence: 99%

Average Neural Face Embeddings for Gender Recognition

Makinist¹,

Ay²,

Aydın³

2020

European Journal of Science and Technology

View full text Add to dashboard Cite

In recent years, with the rise of artificial intelligence and deep learning, facial recognition technologies have been developed that operate with high accuracy even in adverse conditions. However, extracting demographic information such as gender, age and race from facial features has been a hot research area. In this study, a new Average Neural Face Embeddings (ANFE) method that uses facial vectors of people for gender recognition is presented. Instead of training deep neural network from scratch, a simple, fast and effective solution has been developed that performs a distance calculation between the average gender vectors and the person's face vector. The method proposed as a result of the study carried out provided a high and successful recognition performance with with 96.47% of the males and 99.92% of the females.

show abstract

Section: Figure 1 Face Recognition Timelinementioning

confidence: 99%

Average Neural Face Embeddings for Gender Recognition

Makinist¹,

Ay²,

Aydın³

2020

European Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

“…where y' and y present the predicted and real age value respectively and N denotes the number of the testing facial images. The purpose from our work is not to extract exactly the age but we look to just classify ages into three ranges, youth (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), senior (31-50) and elderly (51-over). The proposed method obtains an MAE of 3.26 years, which is considerable very important compared with other methods.…”

Section: Age Gender and Ethnicity Recognition (Ager)mentioning

confidence: 99%

“…Table 3. Comparison of gender accuracy with the state-of-the-art methods (%) Method Year Accuracy Duan et al [8] 2017 88.20 Guo et al [10] 2014 98.40 Dhomne et al [16] 2018 95.00 Srinivas et al [17] 2017 84.70 Lee et al [20] 2017 88.50 Huang et al [21] 2017 89.60 Benini et al [22] 2019 98.59 Fang et al [23] 2019 98.80 Proposed method -95.00 Table 4. Accuracy of ethnicity recognition for MORPH II dataset (%) Method Year Accuracy Guo et al [10] 2014 99.00 Uddin et al [13] 2016 95.40 Srinivas et al [17] 2017 33.33 Mohammed et al [18] 2019 93.3 Hocquet et al [24] 2016 97.50 Mohammed et al [25] 2017 94.60 Proposed method -98.20…”

Section: Age Gender and Ethnicity Recognition (Ager)mentioning

confidence: 99%

Semi-supervised auto-encoder for facial attributes recognition

2020

View full text Add to dashboard Cite

The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II database

show abstract

“…Latterly, CNNs have achieved a significant breakthrough in computer vision fields. Additionally, the CNNs proved to have high ability to obtain the efficient features needed for image classification process [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Architecture for Plant Seedling Classification

Elnemr¹

2019

IJACSA

View full text Add to dashboard Cite

Weed control is a challenging problem that may face crops productivity. Weeds are perceived as an important problem because they conduce to reduce crop yields due to the expanding competition for nutrients, water, and sunlight besides they serve as hosts for diseases and pests. Thus, it is crucial to identify weeds in early growth in order to avoid their side effects on crops growth. Previous conventional machine learning technologies exploited for discriminating crops and weeding species faced challenges of effectiveness and reliability of weed detection at preliminary stages of growth. This work proposes the application of deep learning technique for plant seedling classification. A new Convolutional Neural Networks (CNN) architecture is designed to classify plant seedlings at their early growth stages. The presented technique is appraised using plant seedlings dataset. Average accuracy, precision, recall, and F1score are utilized as evaluation metrics. The results reveal the capability of the proposed technique in discriminating among 12 species (3 crops and 9 weeds). The system achieved 94.38% average classification accuracy. The proposed system is compared with existing plant seedling systems. The results demonstrate that the proposed method outperforms the existing methods.

show abstract

Gender Recognition Through Face Using Deep Learning

Cited by 70 publications

References 5 publications

Average Neural Face Embeddings for Gender Recognition

Average Neural Face Embeddings for Gender Recognition

Semi-supervised auto-encoder for facial attributes recognition

Convolutional Neural Network Architecture for Plant Seedling Classification

Contact Info

Product

Resources

About