2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

Kamencay, Patrik; Hudec, Róbert; Benčo, Miroslav; Zachariášová, Martina

doi:10.5772/58251

Cited by 21 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A prominent algorithm based on fusion of 2D and 3D features is proposed in the study [54] which uses PCA, employing canonical correlation analysis (CCA) to learn mapping between a 2D image and its respective 3D scan. The algorithm is capable of classifying a probe image (whether it is 2D or 3D), by matching it to a gallery image, modeled by fusion of 2D and 3D modalities containing features from both sides.…”

Section: D Face Recognition Algorithmsmentioning

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

Section: D Face Recognition Algorithmsmentioning

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

show abstract

“…A prominent algorithm based on fusion of 2D and 3D features is proposed in the study [33] employing PCA and canonical correlation analysis (CCA). The drawback of this algorithm is that it has been tested only on a single already aligned face database and, therefore, needs testing using unaligned face images from other 3D face databases.…”

Section: Related Workmentioning

confidence: 99%

“…Our 3D FR based study is focused on using multi-view face images. Inspired by the studies [30][31][32], the proposed FRS targets to enhance classification accuracies using, complementary information obtained from synthesized multi-view faces, and fusion strategies (motivated by [33][34][35][36]10,2,28]). The results obtained from our proposed methodology are better than the results reported by other state-of-the-art studies [2,3,10,[21][22][23][24] in terms of all the evaluation criteria.…”

Section: Related Workmentioning

confidence: 99%

Pose and Expression Invariant Alignment based Multi-View 3D Face Recognition

Ratyal¹,

Taj²,

Bajwa³

et al. 2018

KSII TIIS

View full text Add to dashboard Cite

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

show abstract

“…Another interesting work is presented by Kamencay et al [21], which shows a methodology using an algorithm of Principal Component Analysis (PCA) combined with Canonical Correlation Analysis (CCA) to learn the mapping between 2D face images and 3D face data. However, these solutions are prohibitive in an embedded environment with limited resources, so we use the combination of Haar Cascade and Eigenface.…”

Section: A Facial Recognitionmentioning

confidence: 99%

An integrated access control and lighting configuration system for smart buildings

Maciel

Cardoso

Ramos

et al. 2017

JCOMSS

View full text Add to dashboard Cite

This article presents an integrated access control and lighting configuration system for smart buildings. The system uses two-factor authentication, one based on face recognition and other on RFID TAG, and identifies the user inside a room and performs an automatic lighting configuration based on user's behavior. The communication among the devices is performed by Radio Frequency using the low to medium frequency spectrum (LMRF), without providing direct Internet access, and, hence, avoiding known Internet security issues. This system can be easily deployed on meeting rooms or offices in business or government buildings. Through the evaluations we observe an acceptable processing execution time, an acceptable communication time and the robustness of the system.

show abstract

2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

Cited by 21 publications

References 18 publications

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

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

Pose and Expression Invariant Alignment based Multi-View 3D Face Recognition

An integrated access control and lighting configuration system for smart buildings

Contact Info

Product

Resources

About