3D face recognition using covariance based descriptors

Hariri, Walid; Tabia, Hedi; Farah, Nadir; Benouareth, Abdallah; Declercq, David

doi:10.1016/j.patrec.2016.03.028

Cited by 50 publications

(30 citation statements)

References 25 publications

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“…(1) For the identification setup, experimental protocol of [46] is considered to perform N vs. N experiments using d-MVWF, d-MVLHF, d-MVRHF, and d-MVAHF images. According to the mentioned protocol, the image "frontal1" belonging to each of 61 subjects is enrolled in the gallery, whereas the images "frontal2," rotated looking down and rotated looking up are used as probe sets.…”

Section: Experiments On Gavabdb Databasementioning

confidence: 99%

“…The study [44] proposed a face recognition approach using 3D keypoint extraction and sparse comparison based similarity evaluation. The algorithm proposed in the study [46] encoded different types of facial features and modalities into a compact representation using covariance based descriptors where face recognition was performed using a geodesic distance based approach. The study [47] presented a 3D face keypoint detection and matching approach based on principle curvatures.…”

Section: Comparison With Existingmentioning

confidence: 99%

“…Bosphorus. The approach presented in the study [27] reported face recognition accuracies employing facial [46] 98.4 [46] 99.2 [46] 86.9 [26] 70.5 [26] 95.1 [60] 100 [62] 84.1 [62] 47.1 [62] 91.1 [62] 98 [39] 100 [47] 100 [47] 98.4 [47] 93.4 [28] 78.7 [28] - depth information and ICP algorithm and the study [47] is mentioned in above paragraph. The face recognition methodology given in the paper [61] extracted local descriptors to perform matching according to differential surface measurements.…”

Section: Comparison With Existingmentioning

confidence: 99%

“…For example, the algorithms presented in state-of-the-art studies [17,[41][42][43] employed FRGC v2.0 database while those presented in Refs. [19,26,28,[44][45][46] are based on both of FRGC v2.0 and GavabDB databases. Similarly, the studies [26,28,47] employed Gav-abDB, Bosphorus, and FRGC v2.0 databases while the study [27] used UMB-DB to evaluate the algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Ratyal

Taj

Sajid

et al. 2019

Mathematical Problems in Engineering

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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.

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Section: Experiments On Gavabdb Databasementioning

confidence: 99%

Section: Comparison With Existingmentioning

confidence: 99%

Section: Comparison With Existingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ratyal

Taj

Sajid

et al. 2019

Mathematical Problems in Engineering

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show abstract

“…Assim, estima-se que haja a possibilidade de extrair essas informações, tornando um reconhecedor de faces mais robusto a expressões faciais diferentes. As imagens 3D oferecem informação de características geométricas da face capazes de melhorar o desempenho de sistemas de reconhecimento em comparaçãoàqueles baseados em imagens 2D [Hariri et al 2016] [Soltanpour et al 2017].…”

Section: Introductionunclassified

Fatiamento em Reconhecimento Facial: Uma Investigação Baseada em Unidades de Ação para possíveis melhorias através da seleção de fatias da face em Nuvens de Pontos 3D

Martins¹,

Soares²

2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Neste artigo e proposta uma investigação para fatiamento de nuvens de pontos 3D, que representam rostos, dentro do problema de reconhecimento de faces. Para isso, e feita uma comparação entre os resultados de classificação com e sem fatiamento das faces, utilizando características geométricas no processo de extração de informação. Também foram comparados dois tipos de segmentação em subnuvens, o fatiamento Triaxial e o fatiamento em Pizza com Superposição - aqui proposto. O objetivo das comparações é investigar se a seleção de regiões específicas da face 3D pode aprimorar os resultados da classificação. Os resultados mostram-se promissores, indicando que um refinamento da técnica pode gerar uma classificação de indivíduos robusta a variações da face.

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Efficient Machine-Learning Based 3D Face Identification System Under Large Pose Variation

Sghaier

Krichen

Elfakki

et al. 2022

Communications in Computer and Information Science

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3D face recognition using covariance based descriptors

Cited by 50 publications

References 25 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

Fatiamento em Reconhecimento Facial: Uma Investigação Baseada em Unidades de Ação para possíveis melhorias através da seleção de fatias da face em Nuvens de Pontos 3D

Efficient Machine-Learning Based 3D Face Identification System Under Large Pose Variation

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