Algorithmic Mechanism Design for Internet of Things Services Market by Yutao Jiao & Ping Wang & Dusit Niyato

Author:Yutao Jiao & Ping Wang & Dusit Niyato
Language: eng
Format: epub
ISBN: 9789811673535
Publisher: Springer Singapore

3.4.1.2 Face Verification

We use real-world face image datasets to offer a face verification service using a deep learning algorithm as an example of perishable services. Using the proposed model for perishable service, the service provider should first evaluate the service quality and customers’ valuation. Then, it can determine the optimal raw data size, optimal update interval. While serving the customers, the optimal price will be calculated for dynamically selecting the winning customers according to the changing service quality, and the service provider needs to update its external data by the optimal update frequency. As introduced in Sect. 3.1.2, there are two phases in the development of the face verification experiment. The first phase is to train the neural network model to extract the features of face images. Specifically, the dataset for feature learning and extraction combines the CASIA-WebFace dataset [19] and FaceScrub dataset [20]. In total, there are 444, 729 face images from 8, 277 people in the training dataset. In the second phase, we use the well-known FG-NET Aging Database [21] to study the impact of the age gap on the performance of face verification. The dataset for verification contains 1, 002 images from 82 people over large age ranges. We assume the customer’s average arriving rate for perishable services. For demonstration purposes, we normalize the data size, i.e., , throughout this section. This experiment indicates the perishability of data and verifies the corresponding quality decay function (3.37).

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