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.
Highlights
IntroductionFace recognition has been widely used in various fields
In recent years, face recognition has been widely used in various fields
To reduce the number of experiments and to optimize the parameters of a CNN architecture for gender image recognition, this study proposes a Taguchi-based convolutional neural network
Summary
Face recognition has been widely used in various fields. Because the face is a very complex and important biological feature, it contains a lot of information, such as gender, age, and expression. Refs [1,2,3,4] Automatic gender and age analysis has produced many business model applications. Demographic data collection and product sales [5]. The intelligent facial recognition system can be based on the big data of the customer’s gender and age, so that the seller can accurately and clearly understand the customer group that purchased this product. This study focuses on gender classification analysis
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