Chapters authored
Face Recognition: Demystification of Multifarious Aspect in Evaluation Metrics
Face recognition has become an interesting research area in the recent era, and blends knowledge from various disciplines such as neuroscience, psychology, statistics, data mining, computer vision, pattern recognition, image processing, and machine learning. A new opportunity is obtained using the application of statistical methods for evaluating the performance of the system. Evaluation methods are the yardstick to examine the efficiency and performance of any face recognition system. Methods for performance evaluation seek to distinguish, compare, and interpret the various factors such as characteristics of subjects, location, illumination, and images. In this chapter, we show how to adapt popular performance measures commonly used in face recognition research, including—precision, recall, F-measure, fallout, accuracy, efficiency, sensitivity, specificity, error rate, receiver operating characteristics (ROC). This work serves as an introduction to performance measures, and as a practical guide for using them in research.
Part of the book: Face Recognition