Surface representations for 3D face recognition

Fabry, Thomas; Smeets, Dirk; Vandermeulen, Dirk

doi:10.5772/8951

Cited by 8 publications

(4 citation statements)

References 90 publications

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“…12. A review of surface representations is given in [123] and [124]. Most surface representations that are used for 3-D face-recognition methods are explicit.…”

Section: A Face Representationsmentioning

confidence: 99%

A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial soft tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes.

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“…12. A review of surface representations is given in [123] and [124]. Most surface representations that are used for 3-D face-recognition methods are explicit.…”

Section: A Face Representationsmentioning

confidence: 99%

A Comparative Study of 3-D Face Recognition Under Expression Variations

Smeets

Claes

Hermans

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

Self Cite

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“…the ability to present the real time objects through logical surfaces [21]. Similarly, a real time system proposed by [22] investigates the possibility to segment the foreground (human body).…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

et al. 2021

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Context-awareness is an essential part of pervasive computing. Video-based human activity recognition (HAR) has arisen as an imperative module to detect user's situation for involuntary facility delivery in context-aware domains. The activity recognition systems are frequently employed for protective and practical health care. Most of the existing works utilize RGB (red, green, and blue) cameras which present confidentiality and security concerns in the health-care domain. The existing approaches also do not sustain their performance results under the presence of a depth camera. Moreover, the accuracy of an HAR system relies on the extraction and selection of the prominent features from the feature space. To address these limitations, in this research, we first employ a depth camera to resolve the confidentiality and security concerns, and propose an unsupervised segmentation algorithm that can accurately segment the human body from the video frame. Then, we propose a new feature selection technique that has the ability to excerpt and select the best features from the feature space. Our proposed feature selection method focuses on the selection of confined features from the series of images and discriminate their category based on reversion (i.e., regression) value. The proposed method extracts and selects the best features through the benefits of forward selection and backward regression algorithms. Finally, we have trained and tested our proposed system by employing hidden Markov model (HMM) to label the activities. The proposed approach presents a significant performance against the existing works using depth camera.

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“…Frequency domain analysis methods (DFT, DWT, DCT) also belong to this group [7,11,15]. In the last group a 3D model of a face is used [12][13][14]. 3D approaches have the potential under uncontrolled conditions [12].…”

Section: The Base Structure Of the Face Recognition Systemmentioning

confidence: 99%

Mechanisms for increasing the efficiency of holistic face recognition systems

Nowosielski

2015

ELECTROTECHNICAL REVIEW

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In holistic face recognition systems face is represented by set of features derived directly from brightness of 2D image pixels. To ensure proper representation of face under variability in head rotation, lighting and expression many templates should be provided. Mechanisms for increasing the efficiency of holistic face recognition systems are considered. By expanding the base structure with additional function blocks, without incorporating intricate feature extraction methods, it is possible to increase the effectiveness of the system. Streszczenie. W systemach rozpoznawania twarzy opartych na cechach holistycznych (globalnych) twarz jest reprezentowana poprzez zbiór cech wyliczanych bezpośredno z jasności pikseli obrazu 2D. Takie rozwiązanie wymaga odpowiedniej liczby wzorców, aby zapewnić właściwą reprezentatywność twarzy przy obrotach głowy, zmiennym oświetleniu i różnorodności wyrazów twarzy. Mechanizmy zwiększania skuteczności rozpoznawania w tego typu systemach są rozważane w artykule. Poprzez rozszerzenie bazowej struktury za pomocą dodatkowych bloków funkcyjnych, bez wprowadzania skomplikowanych metod ekstrakcji cech, możliwa jest poprawa skuteczności działania systemu (Mechanizmy poprawy skuteczności holistycznych systemów rozpoznawania twarzy).

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Surface representations for 3D face recognition

Cited by 8 publications

References 90 publications

A Comparative Study of 3-D Face Recognition Under Expression Variations

A Comparative Study of 3-D Face Recognition Under Expression Variations

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

Mechanisms for increasing the efficiency of holistic face recognition systems

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