Top PDF Face recognition in low resolution video sequences using super resolution

Face recognition in low resolution video sequences using super resolution

Face recognition in low resolution video sequences using super resolution

other challenges, face recognition from low resolution data video reduces the performance of the existing systems significantly. The goal of this thesis is to develop a system that uses SR as an intermediate step for face recognition in low resolution video and to analyze the face recognition rate improvements. Multi-frame SR is a practical solution that may increase frame resolution, which could in turn improve face recognition rates. The approach of this thesis uses a set of mutually unregistered low resolution face frames captured from video to construct a new frame which is higher in resolution (e.g., see Figure 1). In practice, such a combination of information from multiple images is not trivial. There are two main problems that need to be solved in a super resolution algorithm. First, all the input images need to be correctly aligned with each other on a common grid. Next, an accurate, sharp image has to be reconstructed from the gathered information. If one of these two steps is not done well, the resulting image is not acceptable, and no gain in resolution could be obtained.
Show more

85 Read more

An evaluation of super-resolution for face recognition

An evaluation of super-resolution for face recognition

We evaluate the performance of face recognition algorithms on images at vari- ous resolutions. Then we show to what extent super-resolution (SR) methods can improve the recognition performance when comparing low-resolution (LR) to high-resolution (HR) facial images. Our experiments use both synthetic data (from the FRGC v1.0 database) and surveillance images (from the SCface data- base). Three face recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP). Two SR methods are evaluated. The first method learns the mapping between LR images and the corresponding HR images using a regression model. As a result, the reconstructed SR images are close to the HR images that belong to the same subject and far away from others. The second method compares LR and HR facial images without explicitly constructing SR images. It finds a coherent feature space where the correlation of LR and HR is maximum, and then compute the mapping from LR to HR in this feature space. The perform- ance of the two SR methods are compared to that delivered by the standard face recognition without SR. The results show that LDA is mostly robust to resol- ution changes while LBP is not suitable for the recognition of LR images. SR methods improve the recognition accuracy when downsampled images are used and the first method provides better results than the second one. However, the improvement for realistic LR surveillance images remains limited.
Show more

8 Read more

Face Recognition at a Distance: a study of super resolution

Face Recognition at a Distance: a study of super resolution

We evaluate the performance of face recognition using images with differ- ent resolution. The experiments are conducted on Face Recognition Grand Challenge version one (FRGC v1.0) database and Surveillance Cameras Face (SCface) Database. Three recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP). To improve the performance of face images with low- resolution (LR), two state-of-art super-resolution (SR) methods are applied. One is called Discriminative Super-resolution (DSR). It finds the relation- ship from low-resolution images to their corresponding high-resolution (HR) images so that the reconstructed super-resolution images would be close to the HR images which belongs to the same subject with them and far away from others. The other SR method uses Nonlinear Mappings on Coherent Features (NMCF). Canonical Correlation analysis is applied to compute the coherent features between the PCA features of HR and LR images. Then Radial Basis Functions (RBFs) is used to find the mapping from LR fea- tures to HR features in the coherent feature space. The two SR methods are compared on both FRGC and SCface databases as well.
Show more

62 Read more

Enhancing face recognition at a distance using super resolution

Enhancing face recognition at a distance using super resolution

Obtaining a higher PSNR does not necessarily contribute to a higher recognition rate since high fidelity reconstruction of low- frequency content may dominate the image. Face recognition degrades when probe faces are of significantly lower resolution than those in the gallery. We used super resolution methods in the spatial domain as proposed by [24] and the standard bi-cubic interpolation method to reconstruct a higher resolution version of the low-resolution probe and then perform matching in the usual way at higher resolution. Table 3 shows the results of identification accuracy for different illumination subsets of the Extended Yale B database. We used adaptive histogram equalization (AHE), developed in [28], to normalize variations in illumination of the super resolved images. The experimental results are mixed. While in some cases there is no significant difference between the SR methods (see results for set 2), in set 1 super resolution by dictionary method as pre-processing result in better accuracy of matching in the LL 3 subband. Rather surprisingly, when LH 3 subband is used for matching images in sets 3 and 4 and to some degree in set5, Bi-cubic interpolation has increased recognition accuracy as much as—if not more—by that achieved with the more complex SR methods. This could be due to the presence blocky artifacts in the non-interpolation based super resolution methods. Which result in degrading the face feature vectors specially in badly lit images.
Show more

9 Read more

Extracting a Good Quality Frontal Face Images from Low Resolution Video Sequences

Extracting a Good Quality Frontal Face Images from Low Resolution Video Sequences

Super resolution [2] is the technique for converting low resolution images into high resolution faces. The SR task is cast as the inverse problem of recovering the original high- resolution image by fusing the low-resolution images, based on reasonable assumptions or prior knowledge about the observation model that converts the high resolution image to the low-resolution ones. The fundamental reconstruction constraints for SR is that recovered image, after applying the same generation model should reproduce the observed low resolution image. SR algorithms can be categorized into four classes. Interpolation-based algorithms register low resolution images (LRIs) with the high resolution image (HRI), and then apply non-uniform interpolation to produce an improved resolution image which is further deblurred. Frequency based algorithms try to dealias the LRIs by utilizing the phase difference among the LRIs.
Show more

5 Read more

Low resolution face recognition in knowledge stream

Low resolution face recognition in knowledge stream

Typically, there are two ways for low- resolution face recognition. The hallucination category aims to reconstruct high-resolution faces before recognition, while the embedding category proposes extracting features directly from low- resolution faces via the embedding schema. In the hallucination category, Kolouri et al. constructed a nonlinear Lagrangian model of high- resolution facial appearance and then found the model parameters that best fit the low-resolution faces. Jian et al. Proposed a framework based on singular value decomposition and performed face hallucination and recognition simultaneously. In a joint face hallucination and recognition framework was proposed based on sparse representation. This framework can synthesize person-specific low- resolution faces for recognition. In a system was proposed to recognize faces by using sparse representation with the specific dictionary involving many natural and facial images. Moreover, deep models like and can generate extremely realistic high-resolution images from low-resolution faces. However, the speed of such hallucination or super resolution based approaches may be a little slow due to the complex high- resolution face reconstruction process, which hinders their direct deployment in real-world scenarios with limited computational resources.
Show more

6 Read more

Face Recognition in Low Resolution Using a 3D Morphable Model.

Face Recognition in Low Resolution Using a 3D Morphable Model.

(Equation 3.15), but uses a multi-resolution AAM where the effective resolution of the model is chosen based on the resolution of the input, hence avoiding significant interpolation of the input image during fitting. The training data is first down-sampled to lower resolutions at different scales. An AAM is then trained at each resolution. The landmarks at lower resolutions are obtained by scaling the landmarks from the HR images. Thus, the mean shapes of the multi-resolution AAM differ only by a scaling factor while the shape basis vectors are exactly the same across different resolutions. During fitting, an appropriate model resolution is chosen based on the resolution of the input image. The authors compared the fitting convergence of models at different resolutions and concluded th a t the best performance is obtained when the resolution of the model is only slightly higher than the input image. Furthermore, a face tracking experiment using a person-specific AAM was performed and the results showed th at when fitting to an LR image, using an AAM with a resolution close to the input yields better performance compared to using a high-resolution AAM. The authors did not apply their method to face recognition in [ 68 ]. Hence, it is not clear whether the parameters estimates obtained with this method are robust enough for recognition. However, the same authors used this approach in a model-assisted framework for super­ resolving facial texture as a pre-processing step for face recognition [108]. An image formation model similar to Equation 3.1 was used where the registration was performed by fitting a multi-resolution AAM on the LR inputs. The authors then used an ap­ proach similar to [40] for super-resolution where the super-resolution criterion function is an LI
Show more

173 Read more

Human Face Super Resolution Based on Hybrid Algorithm

Human Face Super Resolution Based on Hybrid Algorithm

Image super-resolution is a classical problem in the domain of computer vision. It aims to infer an HR image with crucial information from the given LR images. Face hallucination is a branch of image super-resolution, which develops do- main specific prior knowledge with strong cohesion to face domain. It was first introduced by Baker and Kanada [1] and has attracted growing attention due to practical importance in many face based applications such as face recognition, face alignment and so on. As the development of machine learning, there are numerous learning-based methods which have been proposed to solve the face hallucination problem. Learning based algorithms have been seen to achieve higher magnification factor with better visual quality than the other super reso- How to cite this paper: Xia, J.F., Yang,
Show more

9 Read more

Face image super-resolution using 2D CCA

Face image super-resolution using 2D CCA

a b s t r a c t In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high- frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low- resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super- resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple eva- luation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches.
Show more

12 Read more

Image Super-Resolution for Improved Automatic Target Recognition

Image Super-Resolution for Improved Automatic Target Recognition

Figure 1. Example sub-pixel displacements. 2. IMAGE SUPER-RESOLUTION THEORY The field of image super-resolution arose from the need to overcome the physical limitations of low-resolution (LR) imaging systems to generate higher-resolution images than would be otherwise possible with the available hardware. For example, in surveillance applications, single video frames are relatively low in image detail and not well-suited to tasks such as face recognition. Similarly, even the high-end optics on imaging satellites are not always sufficient to distinguish important scene features. Fortunately, when a moderate amount of scene motion exists between frames, the data in these low-resolution images can be fused to yield an image of higher resolution than any one of the frames. A variety of approaches can be found in the literature for exploiting this scenario, and a comprehensive overview of the state-of-the art in image SR can be found in, 1 to which we refer the interested reader. For the purpose of a tutorial for the ATR community, a brief overview of 1 is presented in this section.
Show more

9 Read more

Surface UP-SR for an Improved Face Recognition Using Low Resolution Depth Cameras

Surface UP-SR for an Improved Face Recognition Using Low Resolution Depth Cameras

Depth facial data may also benefit from the SR framework. Recently, Berretti et al. proposed to use SR on facial depth images once back-projected in 3-D, and defined the super- faces approach [9]. The SR algorithm they deployed is sim- ilar in principle to the initial blurred estimate provided in the enhanced Shift & Add algorithm proposed by Al Is- maeil et al. in [7]. Later on, this work was extended to the dynamic case where the considered multiple realizations were ordered frames constituting a video sequence [8]. This approach is referred to as Upsampling for Precise Super- Resolution (UP-SR). Its key component is a prior upsam- pling of the observed data which is proven to enhance the registration of frames over time. In addition, it uses a bi- lateral total variation framework as a smoothness condition. In [16], a similar concept of temporal fusion was considered for 3-D facial data enhancement. However, the increase in resolution was induced from temporal data cumulation without a real SR formulation or upsampling. Moreover, smoothness was ensured by bilateral filtering as a post pro- cessing operation and not included in the optimization ob- jective function.
Show more

6 Read more

Low Resolution Face Recognition in Surveillance Systems

Low Resolution Face Recognition in Surveillance Systems

Based on these observations, we propose an approach for face recognition in real surveillance environment. In this paper we focus on the indoor surveillance environment, e.g., in a corridor where people’s motions are generally walking in a single direction in a relatively slow and steady pace. Our focus is hence on face recognition on surveillance captured face images with low resolutions, varied illumina- tion conditions, small pose variation, and slow motions. Due to the very low resolution of the captured face images, many face features are lost. Image pre-processing ideas are employed to remove illumination variations as much as possible. In order to accumulate more features, we fuse a video sequence into one frame in the frequency domain. Curvelet features are adopted in the fusion process. The image is further improved through image super-resolution methods in order to increase the image resolution. Experi- mental results demonstrate that the proposed approach is able to improve the face recognition performance.
Show more

8 Read more

Super-Resolution Methods for Digital Image and Video Processing

Super-Resolution Methods for Digital Image and Video Processing

Abstract Super-resolution (SR) represents a class of signal processing methods allowing to create a high resolution image (HR) from several low resolution images (LR) of the same scene. Therefore, high spatial frequency information can be recovered. Applications may include but are not limited to HDTV, biological imaging, surveillance, forensic investigation. In this work, a survey of SR methods is provided with focus on the non-uniform interpolation SR approach because of its lower computational demand. Based on this survey eight SR reconstruction algorithms were implemented. Performance of these algorithms was evaluated by means of objective image quality criteria PSNR, MSSIM and computational complexity to determine the most suitable algorithm for real video applications. The algorithm should be reasonably computationally efficient to process a large number of color images and achieve good image quality for input videos with various characteristics. This algorithm has been successfully applied and its performance illustrated on examples of real video sequences from different domains.
Show more

57 Read more

Super-Resolution from Image Sequences - A Review

Super-Resolution from Image Sequences - A Review

Degradation Models: Accurate degradation (observa- tion) models promise improved SR reconstructions. Sev- eral SR application areas may benefit from improved degra- dation modeling. Only recently has color SR reconstruc- tion been addressed [16]. Improved motion estimates and reconstructions are possible by utilizing correlated infor- mation in color bands. Degradation models for lossy compression schemes (color subsampling and quantization effects) promise improved reconstruction of compressed video. Similarly, considering degradations inherent in mag- netic media recording and playback are expected to improve SR reconstructions from low cost camcorder data. The re- sponse of typical commercial CCD arrays departs consider- ably from the simple integrate and sample model prevalent in much of the literature. Modeling of sensor geometry, spatio-temporal integration characteristics, noise and read- out effects promise more realistic observation models which are expected to result in SR reconstruction performance im- provements.
Show more

5 Read more

Improved Face Recognition approach Using ILTP for Low Resolution Images

Improved Face Recognition approach Using ILTP for Low Resolution Images

applications, such as access to top security domains, may even necessitate the forgoing of the nonintrusive quality of face recognition by requiring the user to stand in front of a 3D scanner or an infra-red sensor. Therefore, depending on the face data acquisition methodology, face recognition techniques can be broadly divided into three categories: methods that operate on intensity images, those that deal with video sequences, and those that require other sensory data such as 3D information or infra-red imagery. The following discussion sheds some light on the methods in each category and attempts to give an idea of some of the benefits and drawbacks of the schemes mentioned therein in general.
Show more

5 Read more

Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences

Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences

We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color super- resolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simul- taneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video se- quences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the presented algorithms, and their strength.
Show more

15 Read more

Resolution enhancement of video sequences by using discrete wavelet transform and illumination compensation

Resolution enhancement of video sequences by using discrete wavelet transform and illumination compensation

One of the most common tools used in image processing, especially in resolution enhancement techniques, is the wavelet transform [13-17]. A 1-level discrete wavelet transform (DWT) of a video sequence’s frame produces a low-frequency subband known as low-low (LL), and 3 high-frequency subbands, low-high (LH), high-low (HL), and high-high (HH), oriented at horizontal (0 ◦ ), diagonal (45 ◦ ), and vertical (90 ◦ ) angles [18]. In this paper, a video superresolution method is proposed. This resolution enhancement technique uses DWT in order to decompose low-resolution input frames. The LH, HL, and HH subbands of the frames are superresolved using bicubic interpolation. At the same time, the input low-resolution frames are superresolved using the Irani and Peleg technique [6]. Illumination inconsistence can be attributed to uncontrolled environ- ments. Because the Irani and Peleg registration technique is used, it is an advantage that the frames used in the registration process have the same illumination. In addition, in this paper, a new illumination compensation method using singular value decomposition (SVD) is proposed. The illumination compensation technique is ap- plied to the frames as the preprocessing stage, and then the Irani and Peleg resolution enhancement technique is implemented on the processed frames. Finally, inverse DWT (IDWT) is used to combine the interpolated high-frequency subbands, obtained from the DWT of the corresponding frames, and their respective super- resolved input frames to reconstruct a superresolved video sequence. For comparison purposes, the methods of Keren et al. [3], Lucchese and Cortelazzo [5], Marcel et al. [8], and Vandewalle et al. [12] were used for registration, followed by various reconstruction techniques such as the robust superresolution technique [19], bicubic interpolation, iterated back projection [6], and structure adaptive normalized convolution [20].
Show more

9 Read more

Effective Video Inpainting Based on Super-Resolution Algorithm

Effective Video Inpainting Based on Super-Resolution Algorithm

ABSTRACT: Image Inpainting is recover missing part of image as Image is save memories of life’s important moments. A Novel framework is one of the model of inpainting in which image inpainting work on scratchy version of image inpainting. Inpainting of the low resolution image is simple than high quality image. It will display high and complex image. Using different image inpainting techniques create high quality of image from low resolution image and collect the high level images. For this purpose our system uses the super resolution algorithm which is responsible for inpainting of single image.
Show more

8 Read more

Video Inpainting Techniques with Super Resolution: A Literature Survey

Video Inpainting Techniques with Super Resolution: A Literature Survey

________________________________________________________________________________________________________ Abstract - Inpainting aims to restore images with partly information loss and tries to make in-painting results as these missing parts in such a way that the reconstructed the video looks natural. The key issue in video completion is to maintain the spatial temporal information. A lot of researchers have worked in the area of video inpainting. Most of the researchers try to maintain either spatial regularity or temporal stability between the frames. But none of them try to maintain both of them in the identical technique with a good quality. There exist convoluted situation where low-quality input images suffer from inadequate resolution with missing regions. Treating super resolution and inpainting simultaneously decreases noise than super resolution after inpainting
Show more

5 Read more

Face recognition with multi-resolution spectral feature images

Face recognition with multi-resolution spectral feature images

Information in the frequency domain is useful in image classification. In [28], a global feature of a scene, named ‘‘spatial envelope’’, is proposed by exploring the dominant spatial structure of a scene. For this global feature, the global energy spectrum is used to develop spectral signatures for each scene category. To capture the textural characteristics of the image in the frequency domain, a variant of the global energy feature is presented further in [29], which explores the statistics of the co-occurrence matrix. Although the spectral feature is specially designed for scene classification, in this paper we present a spectral representation of face images and apply this representation to the one-sample-per- person problem. One issue with the one-sample-per-person problem is that the number of training sample available is too few. In this paper, multi-resolution spectral images are extracted and used as representations of training face images by means of a method similar to [28], thereby enlarging the size of the training set greatly. We find that, among these spectral feature images, features extracted from some specific orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. Inspired by this finding, in our algorithm the spectral features are used as a robust representation of faces. As we do not know exactly which orientations and scales are robust for all testing images, an alternative approach is to use all of these filters in the decision-making process. In our method, each of the filters will form one weak classifier. The strategy of classifier committee learning (CCL) is designed further to combine the results obtained from different spectral feature images to determine the classes of the testing images. With the strategy of CCL, on the one hand, most of the correct categorizations can be retained. On the other hand, it is not necessary for us to choose the optimal filters, which is a very difficult task for the one-sample-per-person problem. Using the above strategies, the negative effects caused by those unfavorable factors, such as variations of illumination and facial expression, can be alleviated greatly in face recognition. Exper- imental results on some standard databases demonstrate the feasibility and efficiency of the proposed method.
Show more

12 Read more

Show all 10000 documents...