Register-My-Face: a tool to register three-dimensional face images

Abstract

Efficient registration across pose is the most challenging research area for accurate recognition of human face images. Authors have discussed a tool that has been developed for registration of face images across poses using the nose tip of the face images. The nose tip has been considered here because of its stability in situations such as variations in pose, expression, etc. The aim of this investigation is to develop a face registration tool called “Register-My-Face” with a working methodology in all the three directions, namely yaw, pitch, and roll. This tool has been developed for three-dimensional (3-D) face registration, which is inspired by analyzing the “depth values” of face range images. The registration of the face is done using a geometrical technique which is based on computing the corresponding rotation in three orthogonal directions. The advantages of the designed tool are that it does not need any training phase for accurate detection of the nose tip, and this method can handle large pose variations, including 90 deg pose variations about the Y-axis in both the positive and negative directions. The method that has been followed to develop this tool is also independent of facial expression, occlusion and illumination variations. Moreover, it quickly detects the nose tip because it does not need to process the entire face surface, but only requires the isolated nose region. The tool has been integrated with three different databases; GavabDB, Bosphorus, and Frav3D, and the investigation highlights the robustness of the tool. Additionally, for exploring the performance of the tool, a SIMULINK model for hardware interface is also developed with a discrete solver and is tested on two different configuration setups and executed in two different execution modes with two simulation stop timings 10.0 and 1.0. This model can proceed according to the algorithm with a minimum of 6.640 s to register an unregistered raw 3-D face scan input image from the Frav3D database. Accuracies of the nose region of 98.87%, 94.44%, and 98.08% for the Frav3D, GavabDB, and Bosphorus databases, respectively, are observed. For nose tip detection, the success rates are 98.91% for the Frav3D database, 98.74% for the GavabDB database, and 96.03% for the Bosphorus database. Based on the success rate of nose tip detection, the registration process is implemented on three databases. Registration accuracy, computed between a neutral and the registered range face image for the Frav3D database is 87.5% for GavabDB and 89.87% for Bosphorus database, and the rate of success is 70.23%.