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Many Face Recognition techniques focus on 2D-2D comparison or 3D-3D comparison, however few techniques explore the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements cross-dimensional comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during compa...
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... To solve the problem here we are used three basic approaches for [14,15] Pose Invariant Face Recognition (PIFR) these approaches are as follows. Content courtesy of Springer Nature, terms of use apply. ...
Major challenge faced by the recent face recognition techniques treat with pose variation during matching. When comparing different person images, the change in facial image caused by motion in the image or due to because of rotation in image is very considerable. Research into Pose Invariant Face Recognition is still an open area in front of developers today. In this paper, we concentrated on PIFR techniques and combined them with other algorithms to achieve better results. Here we are using the Harris Corner Detection model. Image alignment and Image tagging also used to get front face images. We also went into more detail about PIFR and its interrelated operations for future implementation. By generalization different tricks to handle the pose on face images and minimize the pose variation evaluating performance of the system, We are also going to calculate the Euler angle and their position change, and fixing the pose variation based on it for future research,' said the researchers.
... In proposed framework we have considered basic theory and algorithm ideas of different subsystems used in proposed coalescence of image alignment and tagging in pose variations of human faces by Harris corner detection model and its application for pose-invariant face recognition system [37]. To solve the problem we have used three basic approaches for Pose Invariant Face Recognition (PIFR) [19,20]. These approaches are as follows. ...
The Major challenge in the recent face recognition techniques is to deal with pose variations during matching as facial image differences occurs due to motion/rotation in image, which is very large. The Pose Invariant Face Recognition is still an open area for developers to find solution. In this paper focus is on PIFR techniques and combined it with other algorithms for enhancing the results. Here we are using the Harris Corner Detection model along with Image alignment and Image tagging to get front face images. By generalization different tricks to handle the pose on face images has minimized the pose variation. On evaluating performance of the system, we have also calculate the Euler angle and their position change and according to it correct the pose variation. The results are in accordance with the expected lines.
... In practical intelligent face recognition applications, operating on 2D images is still widely performed. In [7], cross dimensional comparison was implemented to solve pose variety issue, where 2D rendered models were constructed from 3D images with different angles. ...
We propose a robust method for 3D face recognition using 3D to 2D modeling and facial curvatures detection. The 3D-2D algorithm permits to transform 3D images into 3D triangular mesh, then the mesh model is deformed and fitted to the 2D space in order to obtain a 2D smoother mesh. Then, we apply Gabor wavelets to the deformed model in order to exploit surface curves in the detection of salient face features. The classification of the final Gabor facial model is performed using the support vector machines (SVM). To demonstrate the quality of our technique, we give some experiments using the 3D AJMAL faces database. The experimental results prove that the proposed method is able to give a good recognition quality and a high accuracy rate.
... For instance a Minolta 910 scanner requires 0.3 seconds to scan the target in the fast mode (about 76K points), and about 1 second to transfer it to the computer. Longer scan times also result in motion-related problems, including poor 2D-3D correspondence [10]. ...
... Pan et al. [19] used the USF HumanID 3D face database to demonstrate the effectiveness of super-resolve 3D Face model. Dahm N. and Gao Y.S. [7] proposed a face recognition approach based on 2D-3D comparison, and the experimental data derived from the USF HumanID 3D database. ...
Intelligent detection of human face image combined with the real-time video monitoring has been applied to improve the secure and protective possibility. The registration is an indispensible step before distinguishing the variation among the images. Neural network (NN) has a strong learning ability from a mass unstructured point cloud even containing noisy data. Neural network has been applied to register 3D reconstructed ear data and 3D surface of bunny and to achieve the better results. Motivated by this idea, NN-based registration method for 3D rigid face image is proposed. This paper presented the proof process of obtaining rotation matrix and translation vector according to the training process of neural network. Then the measure index of registration performance was provided. The elaborate experiments were conducted on the 3D USF face database (provided by the University of South Florida) to verify the effectiveness of neural network as a registration method. Next, two comparisons were performed, one group was NN-based and ICP-based registration methods and the other group was our proposed NN-based and other NN-based registration methods. The experimental results showed that neural network is a robust and potential registration method for rigid face image registration. Furthermore, our proposed NN-based registration method is extended easily to do 2D-to-3D registration and non-rigid face registration.
... Interested reader may read the comprehensive survey paper by Yang, et al [21]. Viola and Jones [22] uses boosted cascade of simple Haar-like features introduced by Papageorgiou [24] and enhanced by Viola [22] and Lienhart [23] for object detection. This is one of the most discernible algorithms in face detection. ...
In the preceding decade, Human face recognition has attracted significant consideration as one of the most effective applications of image analysis and understanding. Face recognition is one of the diverse techniques used for identifying an individual. Generally the image variations because of the change in face identity are less than the variations among the images of the same face under different illumination and viewing angle. Illumination and pose are the two major challenges, among the several factors that influence face recognition. Pose and illumination variations severely affect the performance of face recognition. Significantly less effort has been taken to tackle the problem of combined variations of pose and illumination in face recognition, though several algorithms have been proposed for face recognition from fixed points. In this paper we propose a face recognition method that is robust to pose and illumination variations. We first propose a simple pose estimation method based on 2D images, which uses a suitable classification rule and image representation to classify a pose of a face image. Then, the image can be assigned to a pose class by a classification rule in a low-dimensional subspace constructed by a feature extraction method. We propose a shadow compensation method that compensates for illumination variation in a face image so that the image can be recognized by a face recognition system designed for images under normal illumination condition. From the implementation result, it is evident that our proposed method based on the hybridization technique recognizes the face images effectively.
... The number of features selected from each voxel region is not constant and depends on their discrimination. (Dahm & Gao, 2010) presented a novel face recognition approach that implements crossdimensional comparison to solve the issue of pose invariance. The approach implements a Gabor representation during comparison to allow for variations in texture, illumination, expression and pose. ...
... Comparison of 3D and 2D representation. Contrast and Brightness have been increased on texture and render for viewing (Dahm & Gao, 2010) Another approach that deals with 3D face recognition was presented by (Cook et al., 2007), where they used multi-scale techniques to partition the information contained in the frequency domain prior to dimensionality reduction. In this manner, it is possible to increase the information available for classification and, hence, increase the discriminative performance of both Eigenfaces and Fisherfaces techniques, which were used for dimensionality reduction. ...
