An evaluation of super-resolution for face recognition

We evaluate the performance of face recognition algorithms on images at various 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 database). 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 performance 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 resolution 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

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

Facial landmark localization in depth images using supervised ridge descent

Berk Gökberk (MEF Author)Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably.WOS:000380434700048Scopus - Affiliation ID: 60105072Conference Proceedings Citation Index- ScienceProceedings PaperAralık2015YÖK - 2015-1

Multi-view reconstruction of 3D human pose with procrustes analysis

Recovery of 3D human pose from cameras has been the subject of intensive research in the last decade. Algorithms that can estimate the 3D pose from a single image have been developed. At the same time, many camera environments have an array of cameras. In this paper, after aligning the poses obtained from single images using Procrustes Analysis, median filtering is utilized to eliminate outliers to find final reconstructed 3D body joint coordinates. Experiments performed on the CMU Panoptic, and Human3.6M databases demonstrate that the proposed system achieves accurate 3D body joint reconstructions. Additionally, we observe that camera selection is useful to decrease the system complexity while attaining the same level of reconstruction performance.IEEE France Section, IEEE Turkey Section, Universite Paris-Saclay, Yeditepe University.WOS:000529320000002Scopus - Affiliation ID: 60105072Conference Proceedings Citation Index- ScienceProceedings PaperKasım2019YÖK - 2019-2

Regional registration for expression resistant 3-D face recognition

Biometric identification from three-dimensional (3-D) facial surface characteristics has become popular, especially in high security applications. In this paper, we propose a fully automatic expression insensitive 3-D face recognition system. Surface deformations due to facial expressions are a major problem in 3-D face recognition. The proposed approach deals with such challenging conditions in several aspects. First, we employ a fast and accurate region-based registration scheme that uses common region models. These common models make it possible to establish correspondence to all the gallery samples in a single registration pass. Second, we utilize curvature-based 3-D shape descriptors. Last, we apply statistical feature extraction methods. Since all the 3-D facial features are regionally registered to the same generic facial component, subspace construction techniques may be employed. We show that linear discriminant analysis significantly boosts the identification accuracy. We demonstrate the recognition ability of our system using the multiexpression Bosphorus and the most commonly used 3-D face database, Face Recognition Grand Challenge (FRGCv2). Our experimental results show that in both databases we obtain comparable performance to the best rank-1 correct classification rates reported in the literature so far: 98.19% for the Bosphorus and 97.51% for the FRGCv2 database. We have also carried out the standard receiver operating characteristics (ROC III) experiment for the FRGCv2 database. At an FAR of 0.1%, the verification performance was 86.09%. This shows that model-based registration is beneficial in identification scenarios where speed-up is important, whereas for verification one-to-one registration can be more beneficial

TurCoins: Türkiye cumhuriyeti madeni para veri kümesi

In this paper, we present a novel and comprehensive dataset which contains Turkish Republic coins minted since 1924 and present a deep learning based system that can automatically classify coins. The proposed dataset consists of 11080 coin images from 138 different classes. To classify coins, we utilize a pre-trained neural network (ResNet50) which is pre-trained on ImageNet. We train the pre-trained neural networks on our dataset by transfer learning. The imbalanced nature of the dataset causes the classifier to show lower performance in classes with fewer samples. To alleviate the imbalance problem, we propose a StyleGAN2-based augmentation method providing realisticfake coins for rare classes. The dataset will be published in http://turcoins.Scopus - Affiliation ID: 6010507

Recognizing non-manual signs in Turkish sign language

Recognition of non-manual components in sign language has been a neglected topic, partly due to the absence of annotated non-manual sign datasets. We have collected a dataset of videos with non-manual signs, displaying facial expressions and head movements and prepared frame-level annotations. In this paper, we present the Turkish Sign Language (TSL) non-manual signs dataset and provide a baseline system for non-manual sign recognition. A deep learning based recognition system is proposed, in which the pre-trained ResNet Convolutional Neural Network (CNN) is employed to recognize question, negation side to side and negation up-down, affirmation and pain movements and expressions. Our subject independent method achieves 78.49% overall frame-level accuracy on 483 TSL videos performed by six subjects, who are native TSL signers. Prediction results of consecutive frames are filtered for analyzing the qualitative results.IEEE France Section, IEEE Turkey Section, Universite Paris-Saclay, Yeditepe University.WOS:0005293200000112-s2.0-85077967469Conference Proceedings Citation Index- ScienceProceedings PaperKasım2019YÖK - 2019-2

Comparison of super-resolution benefits for downsampled images and real low-resolution data

Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy