Occlusion detection and restoration techniques for 3D face recognition: a literature review

Dagnes, Nicole; Vezzetti, Enrico; Marcolin, Federica; Tornincasa, Stefano

doi:10.1007/s00138-018-0933-z

Cited by 45 publications

(20 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3D facial surface is [22] encoded into an indexed collection of radial strings emanating from the nose tip. en, a partial matching mechanism effectively eliminated the occluding parts.…”

Section: D Face Recognitionmentioning

confidence: 99%

Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

Dagher

Al-Bazzaz

2019

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

is paper enhances the recognition capabilities of the facial component-based techniques using the concepts of better Viola-Jones component detection and weighting facial components. Our method starts with enhanced Viola-Jones face component detection and cropping.e facial components are detected and cropped accurately during all pose-changing circumstances. e cropped components are represented by the histogram of oriented gradients (HOG). e weight of each component was determined using a validation process. Combining these weights was done by a simple voting technique. ree public databases were used: the AT&T database, the PUT database, and the AR database. Several improvements are observed using the weighted voting recognition method presented in this paper.

show abstract

“…3D facial surface is [22] encoded into an indexed collection of radial strings emanating from the nose tip. en, a partial matching mechanism effectively eliminated the occluding parts.…”

Section: D Face Recognitionmentioning

confidence: 99%

Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

Dagher

Al-Bazzaz

2019

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

show abstract

“…In our daily life, the faces of criminal suspects are often blocked through camouflage, which causes certain difficulties in face recognition. Aiming at this problem, a detection method and repair strategy are proposed by providing a database and corresponding experiment [15]. Generally speaking, these 3D face recognition algorithms are divided into the following three main categories: spatial-based direct matching, overall feature matching, and local feature matching.…”

Section: Introductionmentioning

confidence: 99%

A New Face Recognition Method for Intelligent Security

Wang

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.

show abstract

“…It has found numerous real-time applications in access control, security, surveillance, criminal identification, fraud detection, and even in human computer interaction [3,4] based on machine learning algorithms [5]. The advantage of face recognition over other biometric modalities is noninvasive acquisition, social acceptance, and appropriateness for noncooperative scenarios [6]. The term face identification is coined for matching a subject's face template with every face template in the gallery [7].…”

Section: Introductionmentioning

confidence: 99%

Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

Ratyal

Taj

Sajid

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

show abstract

Occlusion detection and restoration techniques for 3D face recognition: a literature review

Cited by 45 publications

References 62 publications

Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

A New Face Recognition Method for Intelligent Security

Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

Contact Info

Product

Resources

About