The Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

Sajid, Muhammad; Ratyal, Naeem Iqbal; Ali, Nouman; Zafar, Bushra; Dar, Saadat Hanif; Mahmood, Muhammad Tariq; Joo, Young Bok

doi:10.1155/2019/8041413

Cited by 30 publications

(14 citation statements)

References 30 publications

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“…This study suggests that right asymmetric face images are less inexplicably affected by aging variations compared to the left asymmetric images. This suggests that right asymmetric face images should be used in accurate age estimation [10].…”

Section: Related Workmentioning

confidence: 99%

A deep learning framework for football match prediction

Rahman

2020

SN Appl. Sci.

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An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches. A dataset is used with the rankings, team performances, all previous international football match results and so on. ANN and DNN are used to explore and process the sporting data to generate prediction value. Datasets are divided into sections for training, validating and testing. By using the proposed DNN architecture, corresponding model performed excellently on predicting the FIFA world cup 2018 matches. This model had predicted 63.3% matches accurately. However, this accuracy can be increased with proper datasets and more accurate information of the teams. The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events. For more accurate performance of the prediction, prior and more information about each team, player and match is desirable.

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Section: Related Workmentioning

confidence: 99%

A deep learning framework for football match prediction

Rahman

2020

SN Appl. Sci.

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“…The extracted features are provided as input to an SVM multi-class classifier utilizing multiple kernel functions to determine their distinguishing capability in classifying faulty and non-faulty bearing signatures. Cross-validation is also performed in SVM to select the best kernel parameters [31][32][33][34].…”

Section: Bearing Test Rigmentioning

confidence: 99%

Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings

2019

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The non-stationary nature of bearing vibrations makes it difficult to extract features from the real-time signature of faulty rolling element bearings (REBs). The present work suggests to improve the diagnostic accuracy of fault detection in REBs by enhancing the mode selection property of variational mode decomposition, manipulating its initialization and input parameters (bandwidth selection parameter, and the number of modes, k ) and then extracting energy entropy features and using cross-validation in support vector machine (SVM) classifier. The alpha values and the number of modes are varied from (100, 1000000) and (4, 10), respectively. Mean absolute error (MAE) is used as the indicator which calculates error values obtained between the respective sum of modes and the original signal. The particular − k combination with the least MAE value is chosen. The above method is tested using REB signals obtained from the bearing prognostics test rig. The results obtained from the proposed approach shows 100% diagnostic accuracy in detecting faulty REB vibration signature using five-fold cross-validation in SVM classifier.

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“…For example, Ratyal et al presented a pose invariant deeply learned multi-view 3D face recognitions using deep convolutional neural network, which yields superior performance as compared to other existing methods [40]. Additionally, CNN was also used to assist makeup-invariant face recognition using augmented face dataset, as well as accurate age estimation by investigating the asymmetric left and asymmetric right face images [41,42]. The practical implementations of CNN heavily reply on parallel processors, such as GPU.…”

Section: Related Workmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

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In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

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The Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

Cited by 30 publications

References 30 publications

A deep learning framework for football match prediction

A deep learning framework for football match prediction

Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings

A novel parallel learning algorithm for pattern classification

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