Abstract:

Objective- Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on Image Distortion Analysis (IDA).

Design/Methodology/Approach- Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine and spoof faces. The proposed approach is extended to multi-frame face spoof detection in videos using a voting based scheme.  We also collect a face spoof database, MSU Mobile Face Spoofing Database (MSU MFSD),using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S and iPad Air).

Limitations- It is  difficulty in separating genuine and spoof faces, especially in cross-database and cross device scenarios.

Practical implications- The system ensures user privacy and  provides better security.

Originality- Two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms state-of-the-art methods in spoof detection.

Keywords- Face Recognition, Spoof Detection, Image Distortion Analysis, Ensemble Classifier, Cross-Database, Cross-Device