Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
Browse
4 results
Search Results
Conference Object Citation - WoS: 1Citation - Scopus: 1Face Recognition With Local Zernike Moments Features Around Landmarks(IEEE, 2016) Gökmen, Muhittin; Gökmen, Muhittin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this paper, a new method that extracts the features from the complex Local Zernike Moments (LZM) images around facial landmarks is proposed. In this method, multiple grids which are in different sizes are located on landmarks and Phase-Magnitude (PM) histograms are calculated in each cells of these grids. The PM histograms are calculated for every component of LZM and the feature vectors are created by concatenating these histograms. By reducing the dimensionality of these vectors using Whitened Principle Component Analysis, more robust descriptors are constructed. It is shown that the state-of-the-art results are obtained in the experiments performed on FERET database using the proposed method. © 2016 IEEE.Article Citation - WoS: 30Citation - Scopus: 44An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification(Elsevier, 2020) Gökmen, Muhittin; Gökmen, Muhittin; Başaran, Emrah; Kamasak, Mustafa E.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityVisible-infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%/30.91% on SYSU-MM01 dataset, and by 9.73%/16.36% on RegDB dataset.Conference Object Citation - WoS: 549Citation - Scopus: 667Human Semantic Parsing for Person Re-Identification(IEEE, 2018) Kalayeh, Mahdi M; Gökmen, Muhittin; Shah, Mubarak; Kamasak, Mustafa E; Gökmen, Muhittin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityPerson re-identification is a challenging task mainly dueto factors such as background clutter, pose, illuminationand camera point of view variations. These elements hinder the process of extracting robust and discriminative representations, hence preventing different identities from being successfully distinguished. To improve the representation learning, usually local features from human body partsare extracted. However, the common practice for such aprocess has been based on bounding box part detection.In this paper, we propose to adopt human semantic parsing which, due to its pixel-level accuracy and capabilityof modeling arbitrary contours, is naturally a better alternative. Our proposed SPReID integrates human semanticparsing in person re-identification and not only considerably outperforms its counter baseline, but achieves stateof-the-art performance. We also show that, by employinga simple yet effective training strategy, standard populardeep convolutional architectures such as Inception-V3 andResNet-152, with no modification, while operating solelyon full image, can dramatically outperform current stateof-the-art. Our proposed methods improve state-of-the-artperson re-identification on: Market-1501 [48] by ~17% inmAP and ~6% in rank-1, CUHK03 [24] by ~4% in rank-1and DukeMTMC-reID [50] by ~24% in mAP and ~10% inrank-1.Article Citation - WoS: 9Citation - Scopus: 11An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition(MDPI, 2018) Gökmen, Muhittin; Gökmen, Muhittin; Kamasak, Mustafa E.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.