Neural Networks in Video-Based Age and Gender Recognition on Mobile Platforms

Kharchevnikova, Angelina S.; Savchenko, Andrey V.

doi:10.3103/s1060992x18040021

Cited by 25 publications

(10 citation statements)

References 24 publications

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“…Gabor filter as input in CNN and Adience dataset were used in [13] and achieved accuracy for age and gender is respectively 61.3% and 88.9%. A video-based implementation was done by using Dempster-Shafer theory to generate classifiers using different datasets such as IMFDB, Kinect, EmotiW 2018 and IJB-A in [14]. They increased the accuracy of the age and gender detection by 2-5% and 5-10% correspondingly.…”

Section: Literature Reviewmentioning

confidence: 99%

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Opu

Koly

Das

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Recognition of age and gender has become a significant part of the biometric system, protection, and treatment. It is widely used for people to access age-related content. It is used by social media in the distribution of layered advertising and promotions to expand its scope. Application of face detection has grown to a great extent that we should upgrade it using various methods to achieve more accurate results. In this paper, we have developed a lightweight deep Convolution neural network model for real-time age and gender prediction. For making the training dataset more diverse, Wiki, UTKFace, and Adience datasets have been merged into one containing 18728 images. Using this vast mixed dataset, we have achieved accuracy of 48.59% and 80.76% for age and gender respectively. Further, the model is tested in real-time. Different experimental investigations on the prepared dataset show that with most recent approaches, our model provides competitive prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Opu

Koly

Das

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

View full text Add to dashboard Cite

show abstract

“…In most research studies all these tasks are solved by independent ConvNets even though it is necessary to solve all of them. As a result, the processing of each facial image becomes time-consuming, especially for offline mobile applications (Kharchevnikova & Savchenko, 2018). In this paper it is proposed to solve all these tasks by the same ConvNet.…”

Section: Multi-output Convnet For Simultaneous Age Gender and Identmentioning

confidence: 99%

“…As the age and gender recognition is performed in the proposed pipeline (Fig. 2) for a set of facial images in a cluster, it was decided to use the known video datasets with age/gender labels in the next experiments in order to test performance of classification of a set of video frames (Kharchevnikova & Savchenko, 2018):…”

Section: Age and Gender Recognitionmentioning

confidence: 99%

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Savchenko

2019

PeerJ Computer Science

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This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in an Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.

show abstract

“…In most research studies all these tasks are solved by independent ConvNets even though it is necessary to solve all of them. As a result, the processing of each facial image becomes time-consuming, especially for offline mobile applications (Kharchevnikova and Savchenko, 2018). In this paper it is proposed to solve all these tasks by the same ConvNet.…”

Section: /19mentioning

confidence: 99%

Section: Computer Sciencementioning

confidence: 99%

Peer Review #2 of "Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet (v0.1)"

2019

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This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. The two-stage approach is proposed, in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition, in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The born year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.

show abstract

Neural Networks in Video-Based Age and Gender Recognition on Mobile Platforms

Cited by 25 publications

References 24 publications

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Peer Review #2 of "Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet (v0.1)"

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