How Data Plays in the Requirements of Face Recognition System: A Concern Driven Systematic Literature Review

Machine learning (ML) system is highly data-dependent. It turns out different behavior and performance by using different training data. Since its behavior and performance are more unpredictable than traditional software. The requirement analysis of ML system should focus on model development and its training. This paper reveals how the model development and training are conducted by reviewing the published papers. Based on the principles by Kitchenham, we propose a concern driven systematic literature review method and choose face recognition facilitated ML system as case study. We identify the concerns and then research questions from data perspective, and answer the questions based on the collected literature data. The results show that the current studies included in review have already recognized the role of data played in ML system. Since there is no standard template and method to report data and the related processing procedure, the content and detail about the data reported vary from study to study and are not systematic, which is a great challenge to both practitioners and users.