Face Image Quality Research Articles

Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

3D face reconstruction is an efficient method for pedestrian recognition in non-cooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. Visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.

Facial nerve image enhancement from CBCT using supervised learning technique.

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Face Image Quality Assessment Based on Learning to Rank

Face image quality is an important factor affecting the accuracy of automatic face recognition. It is usually possible for practical recognition systems to capture multiple face images from each subject. Selecting face images with high quality for recognition is a promising stratagem for improving the system performance. We propose a learning to rank based framework for assessing the face image quality. The proposed method is simple and can adapt to different recognition methods. Experimental result demonstrates its effectiveness in improving the robustness of face detection and recognition.

Design and evaluation of photometric image quality measures for effective face recognition

The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the author's studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.

Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver’s license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a person of interest, often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3-D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, this paper considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3-D face models, and incorporation of quality measures for fusion and video frame selection.

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150m probe vs. 1m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.

Image Processing for Face Recognition Rate Enhancement

In this paper, the impact of face image pre-processing in rising the face recognition rate is presented, considering the face images with low contrast, bad or dark lighting. Three preprocessing steps including image adjustment, histogram equalization and image conversion to Joint Photographic Experts Group (JPEG) or (JPG) and Bitmap (BMP) are used to enhance the contrast and the quality of face images respectively. For dimension reduction and feature extraction purposes many techniques are adopted such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principle Component Analysis KPCA and Kernel Fisher Analysis (KFA) and are used to evaluate the effect of illumination variations and image file formats on these techniques. Our results show that the proposed face databases in JPG and BMP formats produced good enhancement and increased the face recognition rate in all techniques when compared with AT&T ORL face database.

Treatment of facial lipoatrophy with polymethylmethacrylate among patients with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS): impact on the quality of life

The lipodystrophy syndrome is characterized by selective loss of subcutaneous fat on the face and extremities (lipoatrophy) and/or accumulation of fat around the neck, abdomen, and thorax (lipohypertrophy). The aim of this study has been to assess the impact of polymethylmethacrylate facial treatment on quality of life, self-perceived facial image, and the severity of depressive symptoms in patients living with HIV/AIDS. A non-randomized before and after interventional study was developed. Fifty-one patients underwent facial filling. The self-perceived quality of life, facial image, and degree of depressive symptoms were measured by the Short-Form 36 and HIV/AIDS--Targeted quality of life questionnaires, by a visual analogue scale and by the Beck depression inventory, respectively, before and three months after treatment. Six of the eight domains of Short-Form 36 and eight of the nine dimensions of the HIV/AIDS--Targeted quality of life questionnaires, together with the visual analogue scale and by the Beck depression inventory scores, revealed a statistically significant improvement. The only adverse effects registered were edema and ecchymosis. The treatment of facial lipoatrophy improved the self-perceived quality of life and facial image as well as any depressive symptoms among patients with HIV/AIDS.

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems. DOI: http://dx.doi.org/10.11591/ijece.v3i6.5034

Pose-Robust Recognition of Low-Resolution Face Images

Face images captured by surveillance cameras usually have poor resolution in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a completely automatic, novel approach for matching surveillance quality facial images to high-resolution images in frontal pose, which are often available during enrollment. The proposed approach uses multidimensional scaling to simultaneously transform the features from the poor quality probe images and the high-quality gallery images in such a manner that the distances between them approximate the distances had the probe images been captured in the same conditions as the gallery images. Tensor analysis is used for facial landmark localization in the low-resolution uncontrolled probe images for computing the features. Thorough evaluation on the Multi-PIE dataset and comparisons with state-of-the-art super-resolution and classifier-based approaches are performed to illustrate the usefulness of the proposed approach. Experiments on surveillance imagery further signify the applicability of the framework. We also show the usefulness of the proposed approach for the application of tracking and recognition in surveillance videos.

Super-Resolution for Surveillance Facial Images via Shape Prior and Residue Compensation

Super-resolution (SR) methods are widely used to enhance the resolution of input images such as low-resolution (LR) remote or surveillance images. The key problem of SR algorithm is to introduce a proper prior for high-resolution (HR) image reconstruction. Usually pixel feature based similarity measure is used as prior. While in real surveillance applications, images are often disturbed by noise so that it is not enough to only use the prior from pixel domain to achieve satisfied high-resolution. In this paper, a two-phase based face hallucination approach via shape prior (SP) is proposed. Firstly, the Active Appearance Model (AAM) is used as shape prior to reconstruct the global face. Then the high frequency information as detailed inartificial facial features of HR face image is compensated to the global face. Experiments show that the proposed algorithm improves the subjective and objective quality of the input LR facial images and outperform many states-of-the- art superresolution methods.

Face image super-resolution using 2D CCA

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 evaluation 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.

16-2 色素成分量推定による顔画像の高画質化技術(第16部門情報ディスプレイ)

16-2 色素成分量推定による顔画像の高画質化技術(第16部門情報ディスプレイ)

Visual illumination compensation for face images using light mapping matrix

Illumination variation is a challenging issue in face recognition. In many conventional approaches the low-frequency coefficients are usually discarded in order to compensate the illumination variations, and hence degrade the visual quality. To deal with these problems, an adaptive normalisation-based method is proposed in this study. Each image is normalised according to its lighting attribute by mapping the low-frequency components to the normal condition instead of discarding them by applying a novel statistical concept called light mapping matrix. The method preserves the low-frequency facial features, maximising the intra-individual correlation and improves the visual quality of face images in different lighting conditions.

Authentications of Myanmar National Registration Card

The automatic identification system of Myanmar national registration card (NRC) holder is presented in this paper. The proposed system can be handled the identification by the extracted low quality face image and fingerprint image from Myanmar NRC. Both of the facial recognition and fingerprint recognition system are developed for Myanmar citizenship confirmation. Age invariant face recognition algorithm is performed based on combination of DiaPCA (Diagonal principal Component Analysis) and KNN (K th nearest neighbor classifier) approaches. An algorithm of the fingerprint recognition is proposed for recognition of the poor quality fingerprint image with fabric background. Several experiments have been done for confirming the effectiveness of the proposed approach.

Face Hallucination Based On Sample Selection Bias Correction

In some learning approach problems, the learning algorithm receives training and test samples drawn according to the same distribution. However, this assumption is not available in practice. In face super-resolution, when the training sample available is biased, it may affect quality of the construction face. In this paper, we proposed a novel method to correct the bias of sample selection in training dataset. First, Active Shape Model is used to get the face shape vectors which contain some information about face contour. Then all faces from Chinese face dataset are classified into certain categories based on Hausdorff Distance by k-means clustering. We correct the sample bias by selecting the most similar faces due to the face shape similarity between samples and test images. Last, the global face reconstruction method based on eigenface is used to achieve satisfied image quality with selected train dataset. Experiments show that the face super-resolution algorithm based on sample selection bias correction can improve the subjective and objective quality of the input low resolution face images compared to traditional eigenface algorithm.

Facial Expression Recognition Analysis with Muti-Scale Filter

The design of filters is the key step of facial expression extraction. Frequency and orientation of the filters can simulate those of the human visual system, and they have the characteristics of being particularly appropriate for texture representation and discrimination. The paper presents the wavelet filter provided with 3 frequencies, 8 orientations. In according to actual demand, it can extract the feature of low quality facial expression image target, and have good robust for automatic facial expression recognition. Experimental results show that the performance of the proposed filter achieved excellent average recognition rates, when it is applied to facial expression recognition system.

Computationally efficient algorithm for face super-resolution using (2D)2-PCA based prior

Super-resolution algorithms typically transform images into 1D vectors and operate on these vectors to obtain a high-resolution image. In this study, the authors first propose a 2D method for super-resolution using a 2D model that treats images as matrices. We then apply this 2D model to the super-resolution of face images. Two-directional two-dimensional principal component analysis (PCA) [(2D) 2 -PCA] is an efficient face representation technique where the images are treated as matrices instead of vectors. We use (2D) 2 -PCA to learn the face subspace and use it as a prior to super-resolve face images. Experimental results show that our approach can reconstruct high quality face images with low computational cost.

Combine image quality fusion and illumination compensation for video-based face recognition

Illumination variation is a critical factor affecting the performance of face recognition especially in video-based face recognition. This paper addresses this problem in two aspects: (i) a novel approach is proposed to classify the illumination orientation so that the uneven illumination on face can be compensated with pertinence. Illumination direction map and plane-fit method are put forward to determine illumination orientations. Experimental results on several public available face databases show that our approach achieved considerable performance gain in contrast to other state-of-the-art methods. (ii) Image quality fusion rule is designed to reduce the influence which is caused by the degradation of facial image quality due to illumination compensation. The degradation can lower the recognition performance. Motivated by human cognitive process and combined with video features, the rule fuses the recognition result of every face video frame to opt best result. This quality fusion rule exhibits effectiveness for illumination compensation, and experimental results on the face video databases with varied illuminations demonstrated that the proposed approach achieved satisfactory recognition rate.

Application notes - Algorithms for Assessing the Quality of Facial Images

In this paper, we presented algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions. We developed face recognition prediction functions for images affected by blurriness, lighting conditions, and head pose variations based upon the eigenface technique. We also developed a classifier for images affected by facial expressions to assess their quality for recognition by the eigenface technique. Our experiments using different facial image databases showed that our algorithms are capable of assessing the quality of facial images. These algorithms could be used in a module for facial image quality assessment in a face recognition system. In the future, we will integrate the different measures of image quality to produce a single measure that indicates the overall quality of a face image