Age and gender classification using wide convolutional neural network and Gabor filter

Hosseini, Sepidehsadat; Lee, Seok Hee; kwon, hyuk jin; Koo, Hyung Il; Cho, Nam Ik

doi:10.1109/iwait.2018.8369721

Cited by 50 publications

(30 citation statements)

References 9 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].…”

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)

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“…In [14], Levi et al used Convolutional Neural Networks (CNN) for age classification and improved the accuracy of classification greatly in comparison with artificial feature extraction scheme. Hosseini et al [15] proposed a convolutional neural network based architecture for joint age-gender classification, where they used the Gabor filter responses as the input. The authors of [15] also concluded that increasing the width of CNN can increase the accuracy of the overall system.…”

Section: Related Workmentioning

confidence: 99%

“…Hosseini et al [15] proposed a convolutional neural network based architecture for joint age-gender classification, where they used the Gabor filter responses as the input. The authors of [15] also concluded that increasing the width of CNN can increase the accuracy of the overall system. In [33], Duan et al proposed an ensemble structure referred to as CNN2ELM, which included CNN and Extreme Learning Machine (ELM).…”

Section: Related Workmentioning

confidence: 99%

“…Then, we apply the DCNNs to extract the feature of the proprocessed facial images. The existing literature [14], [15] demonstrated that DCNNs had superior performance on image recognition compared with the artificial feature extraction methods [16], [17]. However, the use of the DCNN approach requires a large amount of data and training time to facilitate a complex end-to-end feature extraction and classification process [18].…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Qian

Huang

et al. 2019

IEEE Access

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Human facial age estimation has been widely used in many computer vision applications, including security surveillance, forensics, biometrics, human-computer interaction (HCI), and so on. We propose a facial age estimation method oriented to non-ideal facial imagery. The method consists of image preprocessing, feature extraction, and age predication. First, we preprocess non-ideal input images in RGB stream, luminance modified (LM) stream, and YIQ stream. Then, we leverage the deep convolutional neural networks (DCNNs) to extract the feature of images preprocessed in each stream. To reduce the training data volume and training complexity, we adopt the transfer learning to build the DCNN structure. With the extracted feature, the weak classifier equipped at every stream is designed to obtain a weak classification prediction of the age range. Moreover, in order to generate estimation, we use the ensemble learning to fuse the three weak classifiers. We design an integrated strategy algorithm based on the combination of voting method and weighted average method. The simulation results show that our proposed algorithm can improve the an exact match (AEM) and an error of one age category (AEO) by 4.75% and 6.75% compared with the best AEM and AEO of the three weak classifiers. Furthermore, in comparison with the unweighted average method, our proposed algorithm can improve the AEM and AEO by 8.68% and 12.79%, respectively. INDEX TERMS Facial age estimation, transfer learning, ensemble learning, deep convolutional neural network, image preprocessing.

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“…The last layer is finally connected to a so-called “fully connected” layer. In addition, the network can have some additional channels for different features like putting RGB channels or an edge or blurred image as additional channels [ 22 , 23 , 24 , 25 , 26 ]. The main idea behind this complex structure is based on filtering non-appropriate data.…”

Section: Introductionmentioning

confidence: 99%

A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

Tavakkoli

Mohsenzadegan

Kyamakya

2020

Sensors

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The core objective of this paper is to develop and validate a comprehensive visual sensing concept for robustly classifying house types. Previous studies regarding this type of classification show that this type of classification is not simple (i.e., tough) and most classifier models from the related literature have shown a relatively low performance. For finding a suitable model, several similar classification models based on convolutional neural network have been explored. We have found out that adding/involving/extracting better and more complex features result in a significant accuracy related performance improvement. Therefore, a new model taking this finding into consideration has been developed, tested and validated. The model developed is benchmarked with selected state-of-art classification models of relevance for the “house classification” endeavor. The test results obtained in this comprehensive benchmarking clearly demonstrate and validate the effectiveness and the superiority of our here developed deep-learning model. Overall, one notices that our model reaches classification performance figures (accuracy, precision, etc.) which are at least 8% higher (which is extremely significant in the ranges above 90%) than those reached by the previous state-of-the-art methods involved in the conducted comprehensive benchmarking.

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Age and gender classification using wide convolutional neural network and Gabor filter

Cited by 50 publications

References 9 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

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

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