Facial recognition metrics

In this paper, we analyze the two factors for FIR in following steps. First, the face images are aligned to the same size, moreover, their illumination is balanced. Second, we extract classic features widely used in face recognition and retrieval, then utilize them in feature matching with different metrics. At last, face retrieval is performed based on the distances calculated with multiple metrics.

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. It is also described as a Biometric Artificial Intelligence based application that can uniquely identify a person by analysing patterns based on the person's facial textures and shape. While initially a form of computer application , it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics.

Many companies are now exploring biometric face recognition as a viable security solution. This innovative technology shows a lot of promise and could revolutionize how we access sensitive information. But as promising as facial recognition is, it does have flaws. User photos can easily be found through social networks and used to spoof facial recognition software.

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.