Segment-wise Quality Evaluation for Identification of Face Spoofing

Abstract

Non-intrusive nature of the face-based recognition technology makes it more popular among hand held devices. Spoof detection in face-based recognition systems has been an important topic of the research in the last decade. Among several techniques available in the literature for liveness detection, image quality measure (IQM) based technique are particularly attractive due to their computational efficiency. In this paper, an approach based on segment-wise computation of image quality measures is proposed to improve the accuracy of detection. Two types of the non-overlapping segments are considered here: 1) rectangular segments of identical sizes, 2) segment based on neighborhood variance. It is found that both approaches exhibit better performance in comparison with other techniques without increasing too much computational complexity. The experiments are carried out with well-known Replay-Attack database to prove its robustness under different conditions.