Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
A Study on Pose-Invariant Face Recognition Methods Ms. Krishnasree.P.A M. Tech in Computer Science & Engineering, Thejus Engineering College,Vellarakkad Abstract: Face recognition is one of the maximum energetic research topics in pattern recognition and computer vision due to its possible applications in authentication systems, law enforcement, humansystem interfaces, etc. most of the evolved face recognition strategies are best capable of recognizing frontal views of faces, assuming that the individual was looking straight into the camera. The face recognition method is suitable for certain applications where the client poses continuously the identical manner from consultation to session. But, a person might not pose to a camera for the cause of being recognized, possibly not even knowing that a face photo is being captured. In these instances it's miles essential for the machine to handle faces with in plane and intensive rotations. Pose invariant face recognition is an essential area of research due to its many real-world applications, particularly in developing a far better reputation device for business and government technology. Keywords— Pose-invariant face recognition, locally linear regression, local patch, virtual frontal view, Markov random fields, Pose normalization
investigation systems , video indexing , witness to face reconstruction, mug shot searching, access control , bank card identification and security monitoring , to name a few. Face recognition systems mainly classified into two categories: Face verification and Face identification systems. Face verification (1:1 matching) is maintaining a man or woman who's he/she while presented with a face image of an unknown person with a claim of identification. Face identity (1: n matching) is the method of figuring out someone's identity via comparing a given image of unknown individual towards a database of images of recognized people.
2. Overview of Face Recognition System The facial recognition process generally has four related phases or steps. The first step in the facial recognition method is the capturing of a face image. This would commonly be done using a still or video camera.
1. Introduction Human beings see so many persons in their life; they often recognize each individual by recalling their physical or behavioral characteristics. Among all the features face plays an important role in identifying persons. Human face recognition system works in coordination with eye and brain. Each face is captured with the aid of eye; then a typical traits of face which includes eye, nostril, eyebrow, scars, mouth, fore head, hair style, form etc. be used as capabilities to recognize an individual with the aid of mind. Recognizing faces is something that human beings usually do smoothly and without much sensible thought, nevertheless it has continued a difficult problem in the area of computer vision. So face recognition is a completely lively studies place from past two decades. The problem of face recognition may be stated as "identifying an individual from the face image". It extents over computer vision, image processing, human computer interaction and pattern recognition. Face recognition has several possible applications such as
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Face detection: Detecting a face in an image has to determine which pixels within the photo is a part of the face and which are not. Usually, techniques that concentrate on facial landmarks , that detect face-like colors in circular regions, or that use normal characteristic templates, had been used to locate faces. Normalization: Once the face has been detected (separated from its historical past), the face desires to be normalized. Which means the image must be standardized in phrases of size, pose, illumination, and so forth. Relative to the pictures in the gallery or reference database. To normalization a probe image, the important thing facial landmarks have to be placed as it should be. The use of those landmarks, the normalization algorithm can reorient the image for moderate versions. Recognition can best succeed if the probe picture and the gallery images are the same in terms of pose orientation, rotation, scale, length, and so on. Feature extraction: In this stage, a mathematical illustration referred to as a Page 555
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
biometric template or biometric reference is generated, that's saved within the database and could form the idea of any recognition task. Face recognition: In final stage the biometric template of the uncertain face is compared with the biometric template of each face in the database and recognize the face when we get a match between these two biometric templates.
3. Challenges of Face Recognition System Face recognition has theoretical challenges due to uncontrolled environs, illumination variations and pose variations. In face recognition, these challenges leads to the problem that variations of the face of the same person due to lighting, pose etc., is often greater than the variations of facial appearance of different persons, and it reduces the recognition rate.
Image Quality: The major necessity of face recognition system is suspects good quality face image and a good quality image is one which is collected under expected conditions. For extracting the image features the image quality is important. Without the precise calculations of facial features the robustness of the approaches will also be lost. Thus even the best recognition algorithm decline as the quality of the image deteriorations. Illumination Problem: Same face seems in a different way because of change in lighting. Illumination can change the appearance of an object significantly. We must conquer abnormal lighting. Pose Variation: Generally, the training data used by face recognition systems are frontal view face images of individuals. Frontal view images contain more precise information of a face than profile or other pose angle images. The problem seems when the framework need to recognize a rotated face using this frontal view training data. User need together different perspectives of an individual in a face database.
4. Methods This section focuses on different methods that can be used to create the virtual frontal view of an image from given nonfrontal face image.Three methods are used. Locally Linear Regression for PoseInvariant Face Regression
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Pose-Invariant Face Recognition Using Random Markov Fields Component-wise Pose Normalization For Pose-Invariant Face Recognition
4.1. Locally Linear Regression for PoseInvariant Face Recognition In this paper[1], the task is formulated as a general prediction problem, which predicts the mapping from a nonfrontal face to its frontal counterpart.in case the given samples are well aligned, there is an approximated linear mapping between two images of one individual captured under flexible poses. This procedure can be properly expressed as follows: from the given the training set, we need to divide each face image into different uniform rectangle patches. These patches can be either overlapped or adjacent. Here the patch size should be neither too large nor too small. A too large patch may cause the break of linear assumption, while a too small patch might result in serious mis-alignment since we use a generic 3-D model. Therefore, a small patch may result in more artifacts. For each frontal patch, its nonfrontal counterpart is expected to contain surface points of the same semantics as those in the frontal patch. This semantic correspondence can be coarsely built by the aid of a general 3-D cylinder face model. In the predicting stage, for a given nonfrontal face image, the same partitioning criterion as the training images with the same pose is applied to acquire several small patches. Then, for each of these nonfrontal patches, its corresponding frontal patch is predicted by using linear regression.it contain two steps. Estimate the reconstruction coefficients for each patch. Then compute the virtual frontal patch. Finally, all the virtual frontal patches are combined together to form the whole virtual frontal view. After nonfrontal face image is converted to virtual frontal view, poseinvariant face recognition can be easily achieved by using the virtual frontal views instead of all the nonfrontal face images. Therefore, LLR can be combined with any face recognition technologies. It is easy to understand from the two-stage analysis of regression that the proposed method is basically a subspace- based method. The virtual frontal view is created by a linear combination of the frontal views in the training set.Compared with warping-based method, LLR handles occlusion naturally, but the virtual views might be not as realistic as warping-based method. Therefore, how to combine it with warping-based method is an interesting future work. One of the limitations of this method is that, it requires separate models for each pose, which implies it requires a great deal of memory to store the learnt mapping matrices. In practice, we must first consider how many distinct pose models should be Page 556
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in built and how these models can be compressed so as to save storage required. Though this method is easy to be implemented because only two eyes are required for face alignment, the pose of the input nonfrontal face image is anticipated to be known. This implies that one has to use a front-end procedure to predict the pose of the input image.
4.2. Pose-Invariant Face Recognition Using Random Markov Fields This paper [2] presents a technique for reconstructing the virtual frontal view from a certain nonfrontal face image by means of Markov Random Fields (MRFs) and an efficient alternate of the Belief Propagation (BP) algorithm. In this approach, the input face image is partitioned into a grid of overlapping patches. Patch size is the most important factor in this method. It must not be either too large or too small. If the patches are too small, they do not contain enough information for estimating the alignment factors, mainly when there are large displacements. A good patch size must provide enough overlapping in order to align equivalent patches between different views. Conversely, if the patch size is too big, alignment factors may not be estimated precisely and blocking effects also appear. A set of possible warps is obtained by aligning each patch in the input image with images from a training database of frontal faces and is estimated to produce the patches at the frontal view. The alignments are made efficiently in the Fourier domain using the Lucas-Kanade (LK) algorithm that can handle illumination deviations. The problem of finding the ideal warps is then expressed into the following optimization problem using an MRF:
4.3. Component-Wise Pose Normalization for Pose-Invariant Face Recognition In this paper [3], the facial images are segmented into seven different-sized patches, including the training set and probe image. Each patch contains one facial component, e.g., eye, the area between the eyes, nose, mouth, cheek. Using this segmentation, we can avoid breaking facial features into pieces. We first represent the given non-frontal image using a linear combination of the training non-frontal images. Estimating the linear combination coefficients for each patch becomes much easier and more accurate than estimating the global one because of the much lower dimension of the patches. This is factor especially important when the given training set is of limited size. Then, using the linear combination coefficients and the training frontal images, we generate the virtual frontal patch of the given patch. After all virtual frontal patches are generated, they are integrated to form the virtual frontal face image. This virtual frontal face image can be used with any face recognition techniques. This method has two merits: First, the patches are more meaningful, and therefore the formation of coarse cross-pose correspondence is easier. The sizes of patches are neither too large nor too small. They are only related to the image size and are assigned automatically; no manual selection of the block size is needed. Also the patch size is not uniform different components have different sizes. Second, we do not break facial components into pieces. Thus, the blocking artifacts introduced by the block-based method will not ruin those facial components that are more important than others in face recognition.
5. Comparative study
Where measures the cost of assigning the affine warp to the patch, while is a smoothness term which measures the cost of inconsistency at the region of overlaps between the patch and patch. if the two patches are four-connected neighbors. The reconstructed frontal face image can then be used with any face recognition method. It does not require manually selected landmarks and no global geometric transformation is needed. These are the advantages of this approach. One common issue for is that here divide both frontal and non-frontal images into the same regular set of local patches. This Partitioning strategy results in the loss of semantic correspondence for some patches when the pose difference is large, therefore the learnt patch-wise warps may lose practical impact.
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This section compares each pose-invariant face recognition methods. The 2D pose normalizationbased methods perform face synthesis by calculating the warps across poses for each piece, patch, or pixel in the face image. They need only restricted or no training data, and they protect the fine textures of the given image. With the raise in pose difference, though, the synthesized face image contains additional vital stretching artifacts as a result of the dense sampling in comparatively small regions. The linear regression model-based methods balance for this limitation but are typically based on severe assumptions regarding the local various structures across pose. They handle a wider range of pose variations and need a reasonable amount of training data. Table 1 shows the evalution summary of these methods.
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in Table 1. Evaluation Summary of Different Categories of PIFR Algorithms
[5] M. X. Zhang and Y. Gao, “Face recognition across pose: A review,” PatternRecognit., vol. 42, no. 11, pp. 2876–2896, 2009.
Method
[6] N. Wang, D. Tao, X. Gao, X. Li, and J. Li, “A comprehensive surveyto face hallucination,” Int. J. Comput. Vis., vol. 106, no. 1, pp. 9–30,2014
Locally Linear Regression
Markov Random Fields
Componentwise Pose Normalization
Pose Estimation
Manual
N/A
Manual
Landmark Detection
Manual
Auto
Manual
Subject Number
68
68
68
Pose Range
±45º
±45º
±45º
Mean Accuracy
94.6
98.8
91.9
6. Conclusion Pose-invariant face recognition is a challenging problem in the field of image analysis and computer vision and that has received a great deal of attention over the last few years. A number of favorable approaches have been proposed to tolerate or compensate image variations brought by pose changes. Though, achieving pose invariance in face recognition still remains an unsolved challenge, which requires ongoing attentions and efforts. This study reviewed some techniques for pose invariant face recognition that uses locally linear regression, markov random fields and component-wise pose normalization. These techniques usually require more than one gallery image to successfully compensate pose variations.
[7] U. Prabhu, J. Heo, and M. Savvides, “Unconstrained pose-invariant facerecognition using 3D generic elastic models,” IEEE Trans. Pattern Anal.Mach. Intell., vol. 33, no. 10, pp. 1952–1961, Oct. 2011 [8] M. W. Lee and S. Ranganath, “Pose-invariant face recognition usinga 3D deformable model,” Pattern Recognit., vol. 36, no. 8, pp.1835–1846, 2003. [9] A. Asthana, T. K. Marks, M. J. Jones, K. H. Tieu, and M. Rohith, “Fullyautomatic pose-invariant face recognition via 3D pose normalization,” in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 937–944. [10] D. Beymer, “Face recognition under varying pose,” Tech. Rep. A. I. Memo, Artif. Intell. Lab., Mass. Inst. Technol., Cambridge, No. 1461, 1993. [11] X. Chai, S. Shan, X. Chen, and W. Gao, “Local linear regression (LLR)for pose invariant face recognition,” in Proc. 7th Int. Conf. Auto. Face and Gesture Recognition, Southampton, U.K., Apr. 2006, pp. 631–636. [12] T. Cootes, G. Edwards, and C. Taylor, “Active Appearance Models,”IEEE Trans. PAMI, vol. 23, no. 6, pp. 681–685, 2001 [13] A. Ashraf, S. Lucey, and T. Chen, “Fast Image Alignment in the FourierDomain,” in Proc. CVPR, June 2010. [14] R. Gross, I. Matthews, and S. Baker, “AppearanceBased Face Recognitionand Light-Fields,” IEEE Trans. PAMI, vol. 26, no. 4, pp. 449–465,2004. [15] S. Prince, J. Elder, J. Warrell, and F. Felisberti, “Tied Factor Analysis forFace Recognition across Large Pose Differences,” IEEE Trans. PAMI,vol. 30, no. 6, pp. 970– 984, 2008.
7. References [1] X. Chai, S. Shan, X. Chen, and W. Gao, “Locally linear regression for pose-invariant face recognition,” IEEE Trans. Image Process., vol. 16, no. 7, pp. 1716–1725, Jul. 2007 [2] H. T. Ho and R. Chellappa, “Pose-invariant face recognition using Markov random fields,” IEEE Trans. Image Process., vol. 22, no. 4, pp. 1573–1584, Apr. 2013. [3] S.Du and R. Ward, “Component-wise pose normalization for pose invariant face recognition ,”In IEEE ICASSP,2009 [4] V. Blanz, P. Grother, P. J. Phillips, and T. Vetter, “Face recognition based on frontal views generated from nonfrontal images,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2. Jun. 2005, pp. 454– 461
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