A survey of local feature methods for 3D face recognition

Picture processing system by computer complex and recognition of human faces

A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample

Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications

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NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

Deep, dense and accurate 3D face correspondence for generating population specific deformable models

We propose a novel geometric framework for analysing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analysing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and illustrates the use of radial facial curves on 3D meshes to mode facial deformation caused by expression, occlusion and variation in poses and to recognize faces despite large expression, in presence of occlusion and pose variations. Here we represent facial surface by indexed collection of radial geodesic curves on 3D face meshes emanating from nose tip to the boundary of mesh and compare the facial shapes by comparing shapes of their corresponding curves. We use elastic shape analysis for comparing shapes of facial curves because elastic matching seems natural for facial deformation and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. Our results match or improve upon the state-of-the-art methods on two prominent databases: GavabDB and Bosporus, each posing a different type of challenges.

S B Puri

Papers

3d face recognition under expressions, occlusions and pose variation