Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits

Dileep, M. R.; Danti, Ajit

doi:10.1080/08839514.2018.1451217

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…However, it is easier to detect whether the person is kid or adult or old. Similarly, CNN's having only three such categories can efficiently predict [24]. One of the methods that CNN is used to train is by taking each age as a category.…”

Section: Cnn Methods Applied In Age Predictionmentioning

confidence: 99%

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

Kathi¹,

Shaik²

2021

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One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.

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Section: Cnn Methods Applied In Age Predictionmentioning

confidence: 99%

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

Kathi¹,

Shaik²

2021

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“…They increased the accuracy of the age and gender detection by 2-5% and 5-10% correspondingly. A research was carried out using feed-forward propagation neural networks at a finer level with 3-sigma control limits in [15]. By using the JAFFE dataset, they gained accuracy of 95% for age and gender detection.…”

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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“…3) The selection of appropriate frame interval that produces enough object motion is difficult. In order to solve these problems, we improve it mainly from the following two aspects: the three-sigma rule [61], and the kurtosis-based frame selection.…”

Section: A Pre-processingmentioning

confidence: 99%

“…A fast reference frame selection algorithm was proposed by Pan et al [39] to make the numerous reference frames computationally straightforward for foreground extraction. For the above idea, we extract foreground by using the frame interval selection and the adaptive thresholding method based on kurtosis in [61], and afterwards both the calculation result of frame difference method and the edge detection image are considered to generate more complete motion edge. On the other hand, the approach in [28] is used to to eliminate the effects of light.…”

Section: A Pre-processingmentioning

confidence: 99%

Face Occlusion Recognition With Deep Learning in Security Framework for the IoT

Mao

Sheng²,

Zhang

2019

IEEE Access

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Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.

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Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits

Cited by 14 publications

References 10 publications

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

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

Face Occlusion Recognition With Deep Learning in Security Framework for the IoT

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