Boosting 3D LBP-Based Face Recognition by Fusing Shape and Texture Descriptors on the Mesh

Werghi, Naoufel; Tortorici, Claudio; Berretti, Stefano; Bimbo, Alberto Del

doi:10.1109/tifs.2016.2515505

Cited by 59 publications

(32 citation statements)

References 45 publications

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“…The authors, point out procuced to interdependent instructions needs to provide higher accuracy result. N. Werghi, and et al [5] C. Tortorici Chang et al [6] accomplished 3D and 2D picture facial information merged score level based and data level considered to improve the performance to facial information. Both are concentrated 189 and 102 objects, used to found that 3D and 2D picture results in good identification attainment correlated to the 2Diamentional picture.…”

Section: \ Fig1 Block Diagram For Multimodal Fusion Methodsmentioning

confidence: 99%

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Vijayakumar.¹,

K²

2019

IJRTE

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Abstract: A fusion scheme in multimodal biometric system is proposed in the name of Multimodal Feature Template Matching (MTM) system derived from the two picture fused values of average point and weighted assessment point. The main objective of MTM algorithm is to design the average point fusion and weighted average point by fusion of Iris and fingerprint features. The MTM algorithm is assigned with avgweights based on their verification accuracy. A hybrid approach of combining biometric image level information id presented. The raw information is merged at image level. This integration addressed, provides a small template and resilience to attacks. However it does not improve the recognition performance, but the performances is also less combination to its unimodal counterpart. Confidence level integration of compatible features of two different uncorrelated biometric traits fingerprint and iris provides sustainable improvement in performance accuracy compared to other integration methods as well as best unimodal system. Fingerprint and iris integrationapproaches presented in this proposed are more robust and reliable.

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Section: \ Fig1 Block Diagram For Multimodal Fusion Methodsmentioning

confidence: 99%

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Vijayakumar.¹,

K²

2019

IJRTE

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“…To judge whether the detected human is the specific human (SH), his/her face is recognized by the FaceNet [19], which is one kind of face recognition [20]- [23]. FaceNet directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.…”

Section: Introductionmentioning

confidence: 99%

Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

et al. 2020

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To fulfill the tasks of human-robot interaction (HRI), how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach by the integration of Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF) is developed. From the outset, the SSD is employed to detect the human up to 8m using the RGB-D camera with 320 × 240 resolution. Afterward the omnidirectional mobile robot (ODMR) is driven to the neighborhood of 2.5∼3.0m such that the depth image can accurately estimate the detected human's pose. Subsequently, the ODMR is commanded to the vicinity of 1.0m and the orientation inside −60∼60 • with respect to the optical axis to identify whether he/she is the SH by the FaceNet. To reduce the computation time of the FaceNet and extend the SH's tracking, the KCF is employed to achieve the task of HRI (e.g., human following). Based on the image processing result, the required pose for searching or tracking (specific) human is accomplished by the image-based adaptive finite-time hierarchical constraint control. Finally, the experiment with the SH, who is far from and on the backside of the ODMR, validates the effectiveness and robustness of the proposed approach. INDEX TERMS Deep learning, human detection, face recognition, visual tracking, omnidirectional mobile robot, adaptive finite-time hierarchical constraint control, human following.

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“…3D shapes make a wide array of new kinds of application possible, e.g., 3D biometrics, 3D medical imaging, 3D remote sensing, virtual reality, augmented reality, and 3D human-machine interaction [13]. As a special application of 3D shape analysis and processing, the key issue of 3D face recognition has also been widely addressed and identified to be much more robust to varying poses and illumination changes [14]. Therefore, pose invariant face recognition using 3D models is proving to be promising especially in case of in-depth pose variations along x-, y-, and z-axis under unconstrained acquisition scenarios of real world [15].…”

Section: Introductionmentioning

confidence: 99%

“…A prominent curve based method employing Riemannian framework is presented in the paper [26], whereas, the study [27] is a representative region based approach for occlusions and missing data handling problem. Hybrid approaches employ both of holistic and local feature-based methods [28] or combination of 2D and 3D images in the face recognition process [14].…”

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

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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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Boosting 3D LBP-Based Face Recognition by Fusing Shape and Texture Descriptors on the Mesh

Cited by 59 publications

References 45 publications

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

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

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