nils-herrmann/clean_scirepeval huggingface dataset:
Biometric authentication using augmented face and random projection

In this paper, we propose a revocable and privacy preserving template of face biometrics based on random projection. The face biometric is augmented and simultaneously projected onto random subspace. The face image vector is augmented by adding the vector whose elements are varying with zero mean. The augmented face vector can provide better accuracy of authentication and privacy preservation. We analyze the similarity, privacy preserving and security properties of the proposed augmented face biometric information in the random projection-domain. To demonstrate the feasibility of the proposed method, detailed theoretical analysis and several experimental results are provided. The results show that our method is able to provide revocability and privacy preservation while offering better authentication accuracy and security.
Boosting Color Feature Selection for Color Face Recognition

This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images.
Predicting Visual Discomfort Using Object Size and Disparity Information in Stereoscopic Images

This paper proposes object-dependent disparity features to predict the visual discomfort in stereoscopic 3-D images. The proposed object-dependent disparity features quantify the level of visual comfort influenced by disparity gradient of nearby objects and object width, respectively. They consist of relative disparity (mean of disparity difference between nearby objects) and object thickness (ratio of mean width to mean absolute disparity of an object). The prediction performance of the proposed disparity features is evaluated using various types of stereoscopic images. Experimental results demonstrate that the combined use of the proposed object-dependent disparity features substantially improve the prediction performance of the conventional disparity magnitude- and spatial complexity-related features. The performance gain ranges from 0.045 to 0.135 of correlation coefficient, compared with the feature combinations used in the conventional visual comfort metrics.
Face annotation for online personal videos using color feature fusion based face recognition

This paper proposes a novel weighted feature fusion in color face recognition (FR) to automatically annotate faces in personal videos. In the proposed FR method, multiple face images (belonging to the same subject) are clustered from a sequence of video frames. To facilitate a complementary effect on improving annotation performance, the grouped faces are combined using the proposed weighted feature fusion. In addition, we make effective use of facial color feature to cope with decrease in annotation performance due to a low-resolution face in personal videos. To evaluate the effectiveness of proposed FR method, more than 40,000 video frames for 10 real-world personal videos are collected from an existing online video sharing website. Experimental results show that the proposed FR method significantly improves annotation performance obtained using conventional grayscale image based FR methods.
Face annotation for personal photos using collaborative face recognition in online social networks

Automatic face annotation (or tagging) facilitates improved retrieval and organization of personal photos in online social networks. In this paper, we present a new collaborative face recognition (FR) method that aims to improve face annotation accuracy. The proposed method makes efficient use of multiple FR engines and databases that are distributed over an online social network. The performance of our collaborative face recognition method was successfully evaluated using the standard MPEG-7 VCE-3 data set and a set of real-world personal photos from the web. The efficacy of the proposed method is demonstrated in terms of comparative annotation performance against non-collaborative approaches utilizing a single FR engine and a single database only.
Measurement of critical temporal inconsistency for quality assessment of synthesized video

This paper proposes a new temporal consistency measure for quality assessment of synthesized video. Disocclusion regions appear hole regions of the synthesized video at virtual viewpoints. Filling hole regions could be problematic when the synthesized video is perceived through multi-view displays. In particular, the temporal inconsistency caused by hole filling process in view synthesis could affect the perceptual quality of the synthesized video. In the proposed method, we extract excessive flicker regions between consecutive frames and quantify the perceptual effects of the temporal inconsistency on them by measuring the structural similarity. We have demonstrated the validity of the proposed quality measure by comparisons of subjective ratings and existing objective metrics. Experimental results have shown that the proposed temporal inconsistency measure is highly correlated with the overall quality of the synthesized video.
Partial Matching of Facial Expression Sequence Using Over-Complete Transition Dictionary for Emotion Recognition

Facial dynamics contain useful information for facial expression recognition (FER). However, exploiting dynamics in FER is challenging. This is mainly due to a variety of expression transitions. For example, video sequences belonging to a same emotion class may have different characteristics in transition duration and/or transition type (e.g., onset versus offset). The temporal mismatches between query and training video sequences could degrade the FER. This paper proposes a new partial matching framework that aims to overcome the temporal mismatch of expression transition. During the training stage, we construct an over-complete transition dictionary where many possible partial expression transitions are contained. During the test stage, we extract a number of partial expression transitions from a query video sequence. Each partial expression transition is analyzed individually. This increases the possibility of matching a partial expression transition in the query video sequence against the partial expression transitions in the over-complete transition dictionary. To make a partial matching subject-independent and robust to the temporal mismatch, each partial expression transition is defined as facial shape displacement between a pair of face clusters. Experimental results show that the proposed method is robust to variations of transition duration and transition type in subject-independent recognition.
Color Compensation for Anomalous Trichromats Based on Error Score of FM-100 Hue Test

In this paper, we propose novel color compensation for anomalous trichromat. The proposed method is to compensate colors defected due to anomalous trichromacy. The proposed color compensation is performed with color deficiency degree measured by total error score of FM-100 Hue test. The relationship between the deficiency degree and color variation is modeled for accurate compensation. Clinical experiments were performed and the results verified the proposed compensation method
Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition

This paper proposes a novel face descriptor based on color information, i.e., so-called local color vector binary patterns (LCVBPs), for face recognition (FR). The proposed LCVBP consists of two discriminative patterns: color norm patterns and color angular patterns. In particular, we have designed a method for extracting color angular patterns, which enables to encode the discriminating texture patterns derived from spatial interactions among different spectral-band images. In order to perform FR tasks, the proposed LCVBP feature is generated by combining multiple features extracted from both color norm patterns and color angular patterns. Extensive and comparative experiments have been conducted to evaluate the proposed LCVBP feature on five public databases. Experimental results show that the proposed LCVBP feature is able to yield excellent FR performance for challenging face images. In addition, the effectiveness of the proposed LCVBP feature has successfully been tested by comparing other state-of-the-art face descriptors.
Temporally consistent hole filling method based on global optimization with label propagation for 3D video

This paper presents a new temporally consistent hole filling method based on global optimization for a synthesized 3D vid-eo.1 For the temporal consistency, the proposed method adaptively utilizes the already filled region in a previous frame under the guidance of motion vectors to fill a hole region in a current frame (i.e., target frame to-be-filled). In addition, when filling the hole region in the target frame, reliable labels are stored and propagated to a next target frame in order to reduce the computational cost of the global optimization. Experimental results show that the proposed method can achieve the temporal consistency and a high computational gain than existing hole filling methods.
Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis

In clinical studies of breast cancer, masses appear as asymmetric densities between the left and the right breasts, which show different breast tissue structures. For classifying breast masses, most researchers have developed hand-crafted bilateral features by extracting the asymmetric information in 2-D mammograms. In digital breast tomosynthesis (DBT), which has 3D volume data, effective bilateral features are needed to detect masses. In this paper, we propose latent bilateral feature representation with 3-D multi-view deep convolutional neural network (DCNN) in the DBT reconstructed volume. The proposed DCNN is designed to discover hidden or latent bilateral feature representation of masses in self-taught learning. Experimental results show that the proposed latent bilateral feature representation outperforms conventional hand-crafted features by achieving a high area under the receiver operating characteristic curve.
Denoising 3D ultrasound volumes using sparse representation

In this paper, a new 3D ultrasound (US) denoising technique that adopts the sparse representation has been proposed for an effective noise reduction in 3D US volumes. The purpose of the proposed method is to reduce image noise while preserving 3D objects edges, hence improving the human interpretation for clinical diagnosis and the 3D segmentation accuracy for further automatic malignancy detection. For denoising 3D US volumes, sparse representation was employed, which has showed an excellent performance in reducing Gaussian noise. It has been well known that US images contain severe multiplicative speckle noise, which has different characteristics compared to the additive Gaussian noise. In this paper, we propose a denoising framework for effectively reducing both Gaussian noise and speckle noise on 3D US volumes. The proposed method removes Gaussian noise using sparse representation. Then, a logarithmic transform is performed to transform the speckle noise into Gaussian noise for applying the sparse representation. To demonstrate the effectiveness of the proposed denoising method, comparative and quantitative experiments had been conducted on a synthesized 3D US phantom data. Experimental results showed that the proposed denoising could improve image quality in terms of denoising measurements.
MPEG-21-based scalable bitstream adaptation using medium grain scalability

H.264/AVC scalable video coding (SVC) aims at providing high video quality to users in heterogeneous multimedia usage environments. In order to create an adapted bitstream in an optimal way, an adaptation decision-taking algorithm is needed. This algorithm has to take into account the capabilities and the constraints of the targeted usage environment, as well as the properties of the scalable bitstream. In this paper, description tools part of MPEG-21 digital item adaptation (DIA) are used to characterize usage environments and scalable bitstreams, such as usage environment description (UED), universal constraints description (UCD), and adaptation quality of service (AdaptationQoS). Further, we also propose an adaptation decision-taking algorithm for the exploitation of medium grain scalability (MGS) in SVC. This algorithm was implemented in an adaptation decision-taking engine (ADTE). Our experimental results demonstrate the efficiency of the proposed adaptation decision-taking algorithm for the exploitation of MGS in SVC.
On some QoS aspects of user profile in universal multimedia access

A UMA system adapts rich multimedia contents to provide a user with the best possible presentation under the constraints of various terminals and network connections. However, the quality of adapted contents may vary widely with respect to the points-of-view of different providers and different users. So, a user should have some control on the adaptation process. We present two user's description tools, the presentation priority preference and the modality conversion preference, which allow a user to have flexible choices on the quality and modality of output contents. We also propose a systematic approach to integrate these user preferences into the adaptation process. These description tools are developed in the process of MPEG-21 standardization.
Distortion Measures in MPEG-Compressed Domain for Multidimensional Transcoding

In order to find the optimal combination of spatio-SNR-temporal transcoding operations, we need to measure the distortion due to the transcoding operations. In this paper, we develop computational methods to calculate the distortion by using only the information extracted directly from the input bitstream through a minimum decoding process in the DCT domain. The objective of our distortion modeling is to estimate the multidimensional distortion before the entire transcoding process
Towards a better understanding of model-free semantic concept detection for annotation and near-duplicate video clip detection

Given the observation that content transformations tend to preserve semantic information, we demonstrated in previous research that model-free semantic concept detection can be successfully leveraged for identifying NDVCs. In this paper, we seek a better understanding of the usefulness of model-free semantic concept detection for both the task of annotation and NDVC detection. In particular, through extensive experiments, we demonstrate that the problem of detecting semantic concepts for the goal of identifying NDVCs is more relaxed than the problem of detecting semantic concepts for annotation purposes: whereas incorrectly detected semantic concepts negatively affect the effectiveness of annotation, they do not negatively affect the effectiveness of NDVC detection, as long as the same incorrect semantic concepts are detected for both the reference and near-duplicate video clips. This observation has practical implications for the design of a video management system that makes use of model-free semantic concept detection for both the purpose of annotation and NDVC detection.
Color Effect on the Face Recognition with Spatial Resolution Constraints

In the practical face recognition (FR) applications, low-resolution faces (20 times 20 pixels or less) are commonly encountered and negatively impact on reliable performance. To overcome low-resolution face problem, we show that face color can significantly improve the performance compared to intensity-based features. The contribution of this paper is twofold. First, a new metric called dasiavariation ratio gainpsila (VRG) is proposed to theoretically prove the significance of color effect on low-resolution faces. Second, we conduct extensive performance comparison studies. In particular, 3,192 color facial images corresponding to 341 subjects, collected from three standard CMU PIE, FERET, and XM2VTSDB face databases, were used to perform comparative studies of color effect on various face resolutions. Experimental results verified that face color feature improves the degraded recognition rate due to low-resolution faces by at least an order of magnitude over intensity-based features.
Enhanced weakly trained frontal face detector for surveillance purposes

Face detection is becoming popular in surveillance applications; however, the need of enormous size face/non-face dataset, large number of features, and long training time are persistent problems. This paper claims that only a subset of the total number of features conserves the major power to detect faces; hence, this subset is capable to detect faces with high detection rate. The proposed detector fuses the results of two classifiers where one is trained with only 40 Haar-like features and the other is trained with only 50 LBP Histogram features. A pre-processing stage of skin-tone detection is applied to reduce the false positive rate. The detector is examined on real-life low-resolution surveillance sequence. Conducted experiments show that the proposed detector can achieve a high detection rate and a low false positive rate. Also, it outperforms Lienhart detector and tolerates wide range of illumination and blurring changes.
Meaningful scene filtering for TV terminals

Nowadays, the progression of digital broadcasting has been toward establishing a user-friendly TV watching environment through personalized broadcasting services. In the consumer domain, video content filtering enables personalized broadcasting service in which a TV viewer obtains desired scenes even from live broadcasts, which cannot possess metadata. In this paper, therefore, we propose a new real-time content filtering system for live broadcasts that can be applied to TV terminals such as the set-top box and personal video recorder. Based on the proposed filtering, the TV viewer can watch not only favorite broadcasts of a main channel, but also get desired scenes from live broadcasts of other channels simultaneously. The usefulness of the proposed broadcasting content filtering is verified with sport videos on a test-bed broadcasting system
Visual comfort assessment of stereoscopic images using deep visual and disparity features based on human attention

This paper proposes a novel visual comfort assessment (VCA) for stereoscopic images using deep learning. To predict visual discomfort of human visual system in stereoscopic viewing, we devise VCA deep networks to latently encode perceptual cues, which are visual differences between stereoscopic images and human attention-based disparity magnitude and gradient information. To extract the visual difference features from left and right views, a Siamese network is employed. In addition, human attention region-based disparity magnitude and gradient maps are fed to two individual deep convolutional neural networks (DCNNs) for disparity-related features based on human visual system (HVS). Finally, by aggregating these perceptual features, the proposed method directly predicts the final visual comfort score. Extensive and comparative experiments have been conducted on IEEE-SA dataset. Experimental results show that the proposed method can yield excellent correlation performance compared to existing methods.
Digital Broadcasting Contents Packaging for Storage Media

In this paper, we propose a package format which binds digital broadcasts with related data such as TV-anytime metadata, additional resources, and usage history to store and reuse them. To achieve that, we adopt MPEG-4 file format as a container and apply a binary format for scenes (BIFS) for representing usage history. In addition, TV-anytime metadata is used to describe broadcasts and to provide the additional resources, e.g., mp3s, jpeg images, as well. To verify the usefulness of the proposed format, we introduce an application scenario and test it on the scenario.
Towards using semantic features for near-duplicate video detection

An increasing number of near-duplicate video clips (NDVCs) can be found on websites for video sharing. These NDVCs often infringe copyright or clutter search results. Consequently, a high need exists for techniques that allow identifying NDVCs. NDVC detection techniques represent a video clip with a unique set of features. Conventional video signatures typically make use of low-level visual features (e.g., color histograms, local interest points). However, low-level visual features are sensitive to transformations of the video content. In this paper, given the observation that transformations preserve the semantic information in the video content, we study the use of semantic features for the purpose of identifying NDVCs. Experimental results obtained for the MUSCLE-VCD-2007 dataset indicate that semantic features have a high level of robustness against transformations and different keyframe selection strategies. In addition, when relying on the temporal variation of semantic features, semantic video signatures are characterized by a high degree of uniqueness, even when a vocabulary with a low number of semantic concepts is in use (for a query video clip that is sufficiently long).
Distortion modeling for inter-operation dependent video transcoding

In this paper, we present distortion modeling for inter-operation dependent video transcoding. To allocate bits more efficiently, the multidimensional factors including spatial. SNR, and temporal and their trade-offs are exploited in terms of the quality of content. The objective of the joint control for the multidimensional video transcoding is to determine the optimal combination of transcoding operations. Based on the statistical analysis of inter-operation dependent distortion, we propose an improved distortion model. In experiments, the effectiveness of our model is shown.
Region matching based on local structure information in ipsilateral digital breast tomosynthesis views

Digital breast tomosynthesis (DBT) is an emerging 3D x-ray imaging modality in breast cancer screening. Clinical studies have been reported that sensitivity of breast cancer detection can be increased by using two ipsilateral DBT views. Matching corresponding regions in the ipsilateral DBT views is important to achieve high detection sensitivity. In this paper, we propose a novel and effective region matching method based on local structure information in the ipsilateral DBT views. In the proposed method, for a given query region, we find corresponding region on target slices by using local descriptor and associated similarity. Experimental results showed that the proposed region matching method achieved the improvement in accuracy of region matching compared with the existing breast compression model-based region matching method.