A complete and fully automated face verification system on mobile devices

Mobile devices have been widely used not only as a communication tool, but also a digital assistance to our daily life, which imposes high security concern on mobile devices. In this paper we present a natural and non-intrusive way to secure mobile devices, i.e. a complete and fully automated face verification system. It consists of three sub-systems: face detection, alignment and verification. The proposed subspace face/eye detector locates the eyes at a much higher precision than Adaboost face/eye detector. By utilizing attentional cascade strategy, the proposed face/eye detector achieves a comparable speed to Adaboost face/eye detector in this “close-range” application. The proposed approach that determines the class-specific threshold without sacrificing the training data for the validation data further boosts the performance. The proposed system is systematically evaluated on O2FN, AR and CAS-PEAL databases, and compared with many different approaches. Compared to the best competitive system, which is built upon Adaboost face/eye detector and ERE approach for face recognition, the proposed system reduces the overall equal error rate from 8.49% to 3.88% on the O2FN database, from 7.64% to 1.90% on the AR database and from 9.30% to 5.60% on the CAS-PEAL database. The proposed system is implemented on O2 XDA Flame and on average it takes 1.03 s for the whole process, including face detection, eye detection and face verification.

► Complete and fully automated face verification system on mobile devices is proposed.
► The proposed system greatly reduces equal error rate of face recognition system.
► Cascade-APCDA detector is more accurate than Adaboost detector.
► Class-specific threshold based on training data is better than global threshold.