Antonio Rama

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This paper presents a new technique for face recognition that can cope with partial occlussion or strong variations in facial expression. The method tries to solve the face recognition problem from a nearholistic perspective. The main idea is to “eliminate” some features which may cause a reduction of the recognition accuracy under occlusion or expression(More)
Face recognition based on 3D techniques is a promising approach since it takes advantage of the additional information provided by depth which makes the whole approach more robust against illumination and pose variations. However, these 3D approaches require the cooperation of the person to acquire accurate 3D data; thus, they are not appropriated for some(More)
In this paper we present a method for 3D face reconstruction based on the use of a four camera acquisition system. Our method determines correspondences between surface patches on different views through a modeling of depth maps based on Markov Random Fields (MRFs). In order to reduce the occurrence of outliers the MRF-based modeling is bound to satisfy the(More)
The main achievement of this work is the development of a new face recognition approach called Partial Principal Component Analysis (P2CA), which exploits the novel concept of using only partial information for the recognition stage. This approach uses 3D data in the training stage but it permits to use either 2D or 3D data in the recognition stage, making(More)
The paper presents a novel face detection and tracking algorithm which could be part of human-machine interaction in applications such as intelligent cash machine. The facial feature extraction algorithm is based on discrete approximation of Gabor Transform, called Discrete Gabor Jets (DGJ), evaluated in edge points. DGJ is computed using integral image for(More)
In last years, Face recognition based on 3D techniques is an emergent technology which has demonstrated better results than conventional 2D approaches. Using texture (180° multi-view image) and depth maps is supposed to increase the robustness towards the two main challenges in Face Recognition: Pose and illumination. Nevertheless, 3D data should be(More)
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(More)
Multimodal 2D+3D face biometrics commonly report that performance improves relative to that of a single modality. Complete 2D and 3D data can be available during training because they are acquired in a controlled scenario. However, in the evaluation scenario, only partial 2D and 3D data can be acquired and hence available for recognition. In this paper we(More)
In our previous work we presented a new 2D-3D mixed face recognition scheme called Partial Principal Component Analysis (P<sup>2</sup>CA). The main contribution of P<sup>2</sup>CA is that it uses 3D data in the training stage but it accepts either 2D or 3D information in the recognition stage. We think that 2D-3D mixed approaches are the next step in face(More)