Pose-Invariant Face Recognition via RGB-D Images

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions

3D face recognition using inception networks for service robots

The field of face recognition has significantly advanced as deep learning methods, such as those using CNNs, continuously show improvements. However, despite face recognition's promising potential, there are still many concerns regarding privacy and safety. Moreover, the first 2D algorithms, besides having good performance, turned out to be influenced by several factors like the environment's lighting conditions, pose, and facial expression of the subjects, compromising the model's accuracy. This work describes the development of a computer vision system using Deep Learning methods to detect and recognise human faces in 3D in real-time. The RGB images and depth maps from several subjects were captured using an Intel RealSense D455, processed, and consequently provided into two independent CNNs, an Inception-Resnet V1 to deal with the RGB images and an Inception V3 to deal with depth maps. The final algorithm was implemented on the anthropomorphic domestic and healthcare service robot CHARMIE (Collaborative Home Assistant Robot by Minho Industrial Electronics) to perform its tasks according to the recognised user.This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. In addition, this work has also been funded through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundacao para a Ciencia e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH)

Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier