Face recognition from unconstrained three-dimensional face images using multitask sparse representation

In order to improve the speed of stitching and information extraction of remote sensing images, based on the UAV (Unmanned Aerial Vehicle) image stitching algorithm, this paper proposes a UAV image matching method based on the combination of SURF (Speeded Up Robust Features) and MASC (M-Estimate Sample Consensus) algorithm. the SURF algorithm is fast, and the MASC algorithm can effectively eliminate mismatch points and has high stability. In this paper, the SIFT (Scale-invariant feature transform) algorithm, SURF algorithm, SURF and RANSAC (Random Sample Consensus) algorithm as well as SURF and MASC algorithm are used to stitch the same two images and compare them respectively. The experimental results show that the lowest image matching error rate is only 2.13% and the matching time is 4.772670s when using the combination of SURF and MASC algorithm for image stitching, which has certain advantages.

Speeded Up Robust Features (SURF) algorithm is a fast robust local feature matching method. Because of the advantage of invariance of scale and rotation, the SURF algorithm has been widely used in the image registration. However, due to the acquired angle, imaging mode, different resolution, SURF algorithm becomes quite difficult in the image registration. This paper presents a normalized SURF algorithm which can reduce the impact of hue difference between remote sensing images. Combined with the idea of two-way matching, the normalized SURF is able to improve the accuracy of the image registration. The experimental results show the normalized SURF algorithm can keep greater accuracy and relatively higher speed of matching than the original SURF algorithm.

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach – SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings – Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value – The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

This study proposes an improved feature-point matching and 3D registration method for augmented reality tourism applications. This method addresses issues such as poor alignment, low stability in complex environments, and low feature-matching accuracy. An improved feature point matching method for tourism images is introduced, which combines the Speeded Up Robust Features (SURF) algorithm with an enhanced Oriented FAST and Rotated BRIEF algorithm. In this method, feature points are initially detected using the SURF algorithm, and their orientation is determined via wavelet response analysis. The Lucas-Kanade optical flow method is employed for feature point tracking. The random sample consensus algorithm is then used to eliminate mistracked points. Furthermore, an augmented reality tourism 3D registration technique based on an improved homography matrix is proposed to overcome the limitations of traditional homography matrices, such as low matching accuracy and registration efficiency. The performance of the proposed method was analyzed through comparative experiments against the SIFT, SURF, and original ORB algorithms under various image transformations, including scale, blur, illumination, and rotation. The correct matching rate and matching time were used as evaluation metrics. Simulation tests were conducted for 3D registration using different 3D models. Registration accuracy and successful registration counts were evaluated under rotational changes. The outcomes indicated that the average correct matching rate of the proposed algorithm is increased by 44.08%, 36.51%, and 16.09% under scale variation than the scale invariant feature transformation algorithm, speeded up robust features algorithm, and unimproved algorithm, respectively. The correct matching rate under fuzzy transformation increased by 33.46%, 19.65%, and 9.35%, respectively. The average registration accuracy of the proposed 3D registration technique was 98.74% under rotational transformation. The outcomes reveal that the study's suggested approach can successfully improve the scene's virtual and real-world fusion effect and offers a fresh approach to the use of augmented reality technology in the travel industry.

위성 SAR 영상의 활용이 증가하면서 영상의 해석 및 융합을 위한 정밀 기하보정에 대한 필요성이 높아지고 있다. 특히 광역감시 목적으로 활용되기 위해 서로 다른 해상도를 갖는 SAR 영상간 정보융합도 활발해지고 있다. 일반적으로 SAR 영상의 기하보정은 위성의 궤도 및 자세정보를 활용하여 수행할 수 있지만 SAR 센서의 궤도 및 시스템 오차, 대상지형 특성에 의한 왜곡으로 인해 추가적인 보정이 필요하게 된다. SAR 영상을 통한 변화탐지나 타 영상과의 융합에 적용하기 위해서는 기하 오차 보정이 반드시 선행되어야 한다. 이를 위해 다수의 지상 기준점을 선정하고 이를 포함하는 기준 영상과 비교하여 원본 영상에서 대응점을 찾는 방식으로 정밀 기하보정을 수행할 수 있다. Speeded Up Robust Feature (SURF) 기법은 쉽고 빠르게 영상의 기준점을 찾을 수 있지만 상대적으로 해상도가 낮고 스펙클 잡음에 영향을 받는 SAR 영상에서는 활용하기가 어렵다고 알려져 있다. 본 논문에서는 SURF 기법을 위성 SAR 영상에 적용할 때 발생할 수 있는 오차를 추출하고 영상 특성에 따른 성능 변화를 분석하였다. SURF 알고리즘의 적용이 가능한 입력 변수의 적정 범위를 제시하고 그에 따른 영상 정합의 오차를 분석하여 중저해상도의 위성 SAR 영상에 대해서도 SURF 기법을 통한 기하 보정 및 영상 정합이 적용될 수 있음을 검증하였다. As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

This paper proposes a VLSI architecture of the optimized Speeded-Up Robust Feature (SURF) algorithm. The SURF algorithm which is widely used in computer vision applications, locates interest points (IPoints) and extracts feature descriptors based on the surrounding gradients. In the proposed approach, the SURF algorithm is modified to make it more suitable for hardware implementation with little accuracy lost compared to OpenSURF, an open source software implementation based on OpenCV. The resource cost and throughput gain are balanced. Word Length Reduction (WLR) is adopted to compress the integral image and reduce the occupied memory resources. The orientations and the feature descriptors of the IPoints are calculated in a more efficient way, which significantly reduces memory accesses. Moreover, the operations are pipelined both in and among the proposed hardware modules. This VLSI architecture has been validated on FPGA (Xilinx Virtex-4 XC4VLX80), which is able to detect IPoints and extract SURF feature descriptors in a VGA (640 × 480) 64 fps video input with a 96 MHz working frequency while dissipating 1.278 W. This throughput is more than double of the ones reported in the latest literatures.

This paper presents an efficient feature detection algorithm based on the classical SURF (Speeded Up Robust Feature) detector. The image features are represented and scored with respect to its local symmetry property. The local symmetry has natural properties of scale and transformation invariants, and also insensitive to illumination change and local noise. By the proposed feature descriptor, the calculation of 64-dimensional vectors in SURF algorithm can be reduced to 16-dimensional vector respectively. The local symmetry score is defined as the sum of minimum distance between each feature point and its neighboring points in an image based on the image intensities. The algorithm is experimented with some real images and the results are compared with the original SURF algorithm to show its improvement.

Farsi font detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts. To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font recognition process. The SURF algorithm suffers from creation of several redundant features during the detection phase. Thus, the presented version employs the redundant keypoint elimination method (RKEM) to enhance the matching performance of the SURF by reducing unnecessary keypoints. Although the performance of the RKEM is acceptable in this task, it exploits a fixed experimental threshold value which has a detrimental impact on the results. In this paper, an Adaptive RKEM is proposed for the SURF algorithm which considers image type and distortion, when adjusting the threshold value. Then, this improved version is applied to recognize Farsi fonts in texts. To do this, the proposed Adaptive RKEM-SURF detects the keypoints and then SURF is used as the descriptor for the features. Finally, the matching process is done using the nearest neighbor distance ratio. The proposed approach is compared with recently published algorithms for FOCR to confirm its superiority. This method has the capability to be generalized to other languages such as Arabic and English.

3D face identification based on the detection and comparison of keypoints of the face is a promising solution to extend face recognition approaches to the case of 3D scans with occlusions and missing parts. In fact, approaches that perform sparse keypoints matching can naturally allow for partial face comparison. However, such methods typically use a large number of keypoints, locally described by high-dimensional feature vectors: This, combined with the combinatorial number of keypoint comparisons required to match two face scans, results in a high computational cost that does not scale well with large datasets. Motivated by these considerations, in this paper, we present a 3D face recognition approach based on the meshDOG keypoints detector and local GH descriptor, and propose original solutions to improve keypoints stability and select the most effective features from the local descriptors. Experiments have been performed to assess the validity of the proposed optimizations for stable keypoints detection and feature selection. Recognition accuracy has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face identification solutions based on 3D keypoints.

How to construct compact and powerful feature descriptors is an important research subject in the fields of machine vision and pattern recognition. After the research on SURF, the issue that the Haar descriptor of Speeded Up Robust Features(SURF) algorithm can not make full use of the information around the neighborhood of the feature points is found. To resolve the issue, this paper proposes a improved SURF based on the original SURF algorithm. The algorithm makes full use of the information around the neighborhood of the sub-block by masking with 3*3 window templates and then constructs the descriptor which can obtain better discriminative power. In addition, aimed at the primary direction of the SURF depends too much on the local region, the paper improves the confirmation of the primary direction. At last, this paper combines the nearest distance and the ratio between the nearest distance and the second nearest distance to match the interest points. The experiment shows that the algorithm proposed in this paper has better discriminative power, and the face recognition rate is enhanced.

Three-dimensional object construction has seen a great deal of activity in the past decade, as has been pointed out in recent surveys, with the advancement of technology and easy availability of the depth sensors the 3D scanning technology has taken off. A wide range of commercial sensors such as Intel RealSense, Microsoft Kinect are being widely used for real time 3D capturing. Efficient 3D face scanning is one of the important areas of 3D scanning. 3D printer compatible texture supported scanning has a wide range of commercial applications. Such methods are also being used for 3D avatars and characterizations for games. Even though several commercial grade applications are available, most of the techniques suffer from background and light variations. Therefore an efficient face scanning technique is of extreme importance. In this paper we propose a 3D face scanning method based on Intel RealSense technology that combines 3D face detection, background segmentation and 3D mesh mapping to produce realistic 3D face model along with texture export. Further the models then can be manipulated using any 3D editing software along with texture and wireframe manipulation.

In this article, we propose a three-dimensional (3D) face recognition approach for depth data captured by Kinect based on a combination of speeded up robust features (SURF) and k-nearest neighbor (KNN) algorithms. First, the shape index maps of the preprocessed 3D faces of the training gallery are computed, then the SURF feature vectors are extracted and used to form the dictionary. In the recognition process, we propose an improved KNN classifier to find the best match. The evaluation was performed using CurtinFaces and KinectFaceDB data sets, achieving rank-1 recognition rates of 96.78% and 94.23%, respectively, when using two samples per person for training.

This paper presents a highly effective and reliable approach for detecting image forgery and identifying manipulated regions in digital images. The proposed method uses a combination of Singular Value Decomposition (SVD) and the Speeded-Up Robust Features (SURF) algorithm, achieving a high degree accuracy of 99.1% for revealed tampering. After an input image is initially divided parallel to partition, then is performed by SVD to extract features with remarkable discriminability, the method is valued based on independent experiments. The norms are calculated, and pixels with the same norm begin to group to identify potentially tampered areas. In order to simplify the detection process, we conduct a weighted comparison among subgroups to distinguish real structures from false ones. Once we discover a suspicious forgery area, the SURF algorithm comes into play to accurately identify the manipulated items. This process uses a keypoint detector, descriptor calculations, the match between points, and geometric checking to improve the accuracy and reliability of forgery localization. Experimental results on different image databases show that this method is effective. It exhibits advanced ability in detecting forgeries, finding objects and locating where they are in an image. Eventually, we hope this work will produce a sturdy forgery detection system and improve the accuracy of recognizing tampered regions. The proposed method is useful in digital forensics and image verification.

OBJECTIVE To compare the registration accuracy of three-dimensional (3D) facial scans for the design of full-arch implant supported restoration by five methods and to explore the suitable registration method. METHODS According to the criteria, ten patients with maxillary edentulous jaw or end-stage dentition requiring implant supported restorations were enrolled in this study. A special rim with individual feature marks reflected appropriate occlusal relationship and esthetic characteristics was made for each patient. Both 3D facial scan data of natural laughter and with opener traction to expose the teeth or occlusal rim of each patient were acquired by facial scan and input to the digital analysis software Geomagic Qualify 2012. The dataset was superimposed by five different methods: seven facial anatomical landmark points alignment, facial immobile area alignment (forehead and nasal area), facial anatomical landmark points and immobile area combining alignment, facial feature points alignment, facial and intraoral feature points alignment with the same local coordinate system. The three-dimensional deviation of the same selected area was calculated, the smaller the deviation, the higher the registration accuracy. The 3D deviation was compared among the three registration methods of facial anatomical landmark points, facial immobile area alignment and the combination of the above two methods. Friedman test was performed to analyze the difference among the three methods (α=0.05). The effect of the aid of the facial and intraoral feature points were evaluated. Paired t test were performed to analyze the difference (P<0.05). RESULTS The average three-dimensional deviation of the selected area after alignment with the facial anatomical landmarks was (1.501 2±0.406 1) mm, significantly larger than that of the facial immobile area best-fit alignment [(0.629 1±0.150 6) mm] and the combination of the two methods[(0.629 1±0.150 6) mm] (P < 0.001). The aid of the facial feature points could significantly reduce the deviation (t=1.001 3, P < 0.001). There was no significant statistical difference in the remaining groups. CONCLUSION The forehead area of the 3D facial scan can be exposed as much as possible. The establishment of facial characteristic landmark points and the use of the invariant area alignment can improve the accuracy of registration. It should be clinically feasible to apply three-dimensional facial scan to the design of full-arch implant supported restoration with the registration of the immobile area on the face especially the forehead area.