3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

Queirolo, Chauã C.; Silva, Luciano; Bellon, Olga Regina Pereira; Segundo, Maurício Pamplona

doi:10.1109/icpr.2008.4761696

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Six parameters are required for simulated annealing (in which three for every translation also the rotation referencing to a 3D coordinate system) which is used to define transformation matrix which is used for an alignment between two 3D faces. is approach aligns images of the face in three phases: (1) alignment in initial level, (2) alignment in an approximate level, and (3) alignment in the last level [14]. Initially, the center of the two-sided mass is being aligned.…”

Section: Simulated Annealing-based Alignmentmentioning

confidence: 99%

A Real-Time Framework for Human Face Detection and Recognition in CCTV Images

Ullah

Hayat

Siddiqui

et al. 2022

Mathematical Problems in Engineering

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This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction algorithms, principal component analysis (PCA) and convolutional neural network (CNN). We use and compare the performance of the algorithms K-nearest neighbor (KNN), decision tree, random forest, and CNN. The recognition is done by applying these techniques to the dataset with more than 40K acquired real-time images at different settings such as light level, rotation, and scaling for simulation and performance evaluation. Finally, we recognized faces with a minimum computing time and an accuracy of more than 90%.

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Section: Simulated Annealing-based Alignmentmentioning

confidence: 99%

A Real-Time Framework for Human Face Detection and Recognition in CCTV Images

Ullah

Hayat

Siddiqui

et al. 2022

Mathematical Problems in Engineering

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“…A single alignment event required for a probe to align it to ICS makes this technique appropriate for identification as well as verification scenarios [15]. SA [32] algorithm employs a stochastic technique using a local search based approach and its drawback is excessive time consumption [33]. Similar to ICP, this method is suitable for verification setup only.…”

Section: D Face Alignment Algorithmsmentioning

confidence: 99%

“…In alignment phase, facial features are transformed such that they can be reliably matched. A few 3D alignment techniques [29][30][31][32][33][34] existing in literature are based on Iterative Closest Point (ICP) [30], Intrinsic Coordinate System (ICS) [31], Simulated Annealing (SA) [32], and Average Face Model (AFM) [29].…”

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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“…The IMAGO Research Group has started developing a 3D face recognition system in 2005. Since then several works were published by our group presenting different components of a 3D face recognition system, such as face reconstruction [9], face localization [8], face segmentation [10], [15], facial landmarks detection [10], [15], face matching [11], [14], [16]- [18] and matching evaluation [12], [13].…”

Section: B 3d Face Recognitionmentioning

confidence: 99%

“…To this end, feature points were employed to extract some rigid regions of the face which are less affected by expressions, as shown in Figure 7. The matching of multiple facial regions is performed in hierarchical manner [11]- [14], [16] to obtain faster and more accurate face verification results. Our group is working on 3D face recognition solutions in more difficult scenarios, such as real-time systems, recognition at distance and complex backgrounds.…”

Section: B 3d Face Recognitionmentioning

confidence: 99%

IMAGO Research Group

Silva

Bellon

Segundo

et al. 2011

JIS

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The IMAGO Research Group was founded in 1996 and accumulated great expertise in range image analysis during its existence. The group has been working on projects related to computer vision, image processing and computer graphics in order to produce solutions for different areas, such as culture, education, social inclusion, health and security. The efforts of the group are currently focused on digital preservation and biometric recognition, in which there are three main ongoing projects: 3D Virtual Museum, 3D Face Recognition and Newborn Identification. These projects have virtual reality and 3D interaction features beyond the HCI requirements, that provides motivation in the use of our systems.

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3D face recognition using the Surface Interpenetration Measure: A comparative evaluation on the FRGC database

Cited by 7 publications

References 20 publications

A Real-Time Framework for Human Face Detection and Recognition in CCTV Images

A Real-Time Framework for Human Face Detection and Recognition in CCTV Images

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

IMAGO Research Group

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