P/sup 2/CA: a new face recognition scheme combining 2D and 3D information

Rama, A.; Tarres, F.

doi:10.1109/icip.2005.1530507

Cited by 15 publications

(14 citation statements)

References 4 publications

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“…To combine the advantages of 2D face image and 3D reconstructed face image, Rama and Tarres proposed a face recognition using Partial PCA (P 2 CA) [1]. A gallery of images is created from a minimal set of pose variation (0°, ±45°°, ±90°) and for classification, varied angles (0°, ±30°, ±45°, ±60°, ±90°) and with different illumination(natural or environment lighting, strong light and frontal mid light) are used.…”

Section: Related Workmentioning

confidence: 99%

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Active Appearance Based Pose and Illumination Variant Face Recognition From Video Sequences

Awaradi¹

2015

IJRET

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Section: Related Workmentioning

confidence: 99%

“…Identifying and recognizing a face from a stored database of images or video is termed as face recognition [1], [3], [4], [5]. The two main challenges in face recognition are illumination and pose variation [2], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Active Appearance Based Pose and Illumination Variant Face Recognition From Video Sequences

Awaradi¹

2015

IJRET

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“…The data is then used to find facial landmarks in different views. A variety of face recognition systems [37,38,39,40,41] use 3D models to synthesize intermediate views or viewpoint invariant reference frames for the purpose of face recognition. A more comprehensive survey on similar methods can be found in [42].…”

Section: Synthetic Training Datamentioning

confidence: 99%

Fast Face Detector Training Using Tailored Views

Scherbaum

Petterson

Feris

et al. 2013

2013 IEEE International Conference on Computer Vision

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Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under controlled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core implementation of Viola Jones' AdaBoost object detection framework. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art approaches on this challenging dataset.

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“…they intend to combine 2D and 3D information for the face recognition problem. Recently, a novel approach called Partial Principal Component Analysis (P 2 CA) has been presented [1]. This approach still presents some problems to cope with illumination changes.…”

Section: Partial Information Conceptmentioning

confidence: 99%

“…In section 2, the fundamentals of the P 2 CA technique presented in [1] are reviewed and extended to the LDA space. Some…”

Section: Partial Information Conceptmentioning

confidence: 99%

Partial LDA vs Partial PCA

Rama

Tarres

2006

2006 IEEE International Conference on Multimedia and Expo

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Recently, 3D face recognition algorithms have outperformed 2D conventional approaches by adding depth data to the problem. However, independently of the nature (2D or 3D) of the approach, the majority of them required the same data format in the test stage than the data used for training the system. This issue represents the main drawback of 3D face research since 3D data should be acquired under highly controlled conditions and in most cases require the collaboration of the subject to be recognized. Thus, in real world applications (control access points or surveillance) this kind of 3D data may not be available during the recognition process. This leads to a new paradigm using some mixed 2D-3D face recognition systems where 3D data is used in the training but either 2D or 3D information can be used in the recognition depending on the scenario. PARTIAL INFORMATION CONCEPTThe performance of face recognition systems that use 2D intensity images depends highly on the conditions during the acquisition of the image, e.g. pose of the face, illumination, or facial expression. Since a face is a 3D object, new face recognition techniques have tried to add shape or depth information to make the system more robust towards pose and lighting variations. Additionally, 3D data acquisition is becoming faster and cheaper by means of special 3D scanner devices or multi-camera systems [2]. Therefore, 3D face recognition research is getting more and more important [3,4,5,6,7]. These 3D algorithms can be roughly divided in two categories: The approaches of the first group basically compute a depth and intensity map separately and then they perform a conventional 2D method to each modality and combine them as two different expert opinions [4,7]. The second category encloses model-based approaches that use complete 3D models of a face to perform the recognition [3,5,6]. The advantage of the first category is that it adds depth information to conventional approaches without increasing too much the computational cost; but, on the other hand, most of them are not true 3D approaches and they should be called 2.5D techniques since they may not have multi-view information. Furthermore, the input of the recognition stage of these approaches should maintain the same data format as the training images, i.e. if frontal views have been used during the training stage then a depth and/or intensity frontal image may be required in the recognition stage [4]. On the contrary, the majority of the model-based 3D face approaches intend to fit texture images on some 3D models. After this adjustment, they extract some relevant features, in most of the cases geometrical parameters, that will be used in the recognition stage. In this case, the input images for the recognition phase could be common 2D intensity images which can be available in any kind of application either under controlled or uncontrolled acquisition conditions. However, the process of fitting an intensity image on a generic model is very computationally demanding and not a very p...

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P/sup 2/CA: a new face recognition scheme combining 2D and 3D information

Cited by 15 publications

References 4 publications

Active Appearance Based Pose and Illumination Variant Face Recognition From Video Sequences

Active Appearance Based Pose and Illumination Variant Face Recognition From Video Sequences

Fast Face Detector Training Using Tailored Views

Partial LDA vs Partial PCA

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