Using Convolutional Neural Networks Based on a Taguchi Method for Face Gender Recognition
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TLDR
An AlexNet network with optimized parameters is proposed for face image recognition and a Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design.Abstract:
In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error method to select CNN parameters. In this study, an AlexNet network with optimized parameters is proposed for face image recognition. A Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design. The proposed method filters out factors that are significantly affected. Finally, experimental results show that the proposed Taguchi-based AlexNet network obtains 87.056% and 98.72% average accuracy of image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than that of the original AlexNet network in CIA and MORPH databases, respectively.read more
Citations
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Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models
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Optimization of the ANNs Predictive Capability Using the Taguchi Approach: A Case Study
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Journal ArticleDOI
An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis
Imène Neggaz,Hadria Fizazi +1 more
TL;DR: In this article , the Archimedes optimization algorithm (AOA) is used to identify the optimal face area using a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning.
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