The FERET verification testing protocol for face recognition algorithms

Phillips, P. Jonathon; Rizvi, Syed A.; Moon, Hyeonjoon

doi:10.6028/nist.ir.6281

Cited by 59 publications

(44 citation statements)

References 11 publications

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“…In the face recognition literature, face orientation has received deliberate attention. The FERET data base [17], for instance, includes both frontal and oblique views, and several specialized data bases have been collected to try to develop methods of face recognition that are invariant to moderate change in face orientation [20]. In the face expression literature, use of multiple perspectives is rare and relatively less attention has been focused on the problem of pose invariance.…”

Section: Head Orientation and Scene Complexitymentioning

confidence: 99%

“…In the absence of comparative tests on common data, the relative strengths and weaknesses of different approaches is difficult to determine. In the areas of face and speech recognition, comparative tests have proven valuable [e.g., 17], and similar benefits would likely accrue in the study of facial expression analysis. A large, representative test-bed is needed with which to evaluate different approaches.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive database for facial expression analysis

Kanade

Cohn

Tian

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)

1,887

1,150

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Section: Head Orientation and Scene Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive database for facial expression analysis

Kanade

Cohn

Tian

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)

1,887

1,150

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“…The same experimental protocol used in (Hajati, 2012) was applied in this work. The performance was measured in terms of rank-1 recognition accuracy and the Cumulative Matching Characteristics (CMC) (Rizvi, 1998). All the faces with the neutral expression were used for training, while the rest of database was used as the testing faces.…”

Section: Facial Expressionmentioning

confidence: 99%

3-D Shape Matching for Face Analysis and Recognition

Quan¹,

Matuszewski²,

Shark³

2015

Proceedings of the International Conference on Pattern Recognition Applications and Methods

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Abstract:The aims of this paper are to introduce a 3-D shape matching scheme for automatic face recognition and to demonstrate its invariance to pose and facial expressions. The core of this scheme lies on the combination of non-rigid deformation registration and statistical shape modelling. While the former matches 3-D faces regardless of facial expression variations, the latter provides a low-dimensional feature vector that describes the deformation after the shape matching process, thereby enabling robust identification of 3-D faces. In order to assist establishment of accurate dense point correspondences, an isometric embedding shape representation is introduced, which is able to transform 3-D faces to a canonical form that retains the intrinsic geometric structure and achieve shape alignment of 3-D faces independent from individual's facial expression. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE databases, which contain faces expressions and pose variations. The performance of the system was compared with the existing benchmark approaches and it demonstrates that the proposed scheme provides a competitive solution for the face recognition task with real-world practicality.

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“…The performance is measured in terms of the Cumulative Match Characteristics (CMC) [9] and the rank-1 recognition rate. In order to demonstrate the performance improvement resulted from the fusion process, we also presented the final fusion result, as well as the result based on ridge (or valley) data only, as illustrated in Fig.…”

Section: B Face Recognitionmentioning

confidence: 99%

Face Recognition Using 3D Directional Corner Points

Gao

Zhou

2014

2014 22nd International Conference on Pattern Recognition

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In this paper, we present a novel face recognition approach using 3D directional corner points (3D DCPs). Traditionally, points and meshes are applied to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derived from ridge and valley curves. Then we develop a 3D DCP matching method to compute the similarity of two different 3D surfaces. This representation, along with the similarity metric can effectively integrate structural and spatial information on 3D surfaces. The added information can provide more and better discriminative power for object recognition. It strengthens and improves the matching process of similar 3D objects such as faces. To evaluate the performance of our method for 3D face recognition, we have performed experiments on Face Recognition Grand Challenge v2.0 database (FRGC v2.0) and resulted in a rank-one recognition rate of 97.1%. This study demonstrates that 3D DCPs provides a new solution for 3D face recognition, which may also find its application in general 3D object representation and recognition.

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The FERET verification testing protocol for face recognition algorithms

Cited by 59 publications

References 11 publications

Comprehensive database for facial expression analysis

Comprehensive database for facial expression analysis

3-D Shape Matching for Face Analysis and Recognition

Face Recognition Using 3D Directional Corner Points

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