Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Comparing Humans and Deep Neural Networks on Face Recognition Under Various Distance and Rotation Viewing Conditions
(Journal of Vision, 2023) Fux, Michal; Arslan , Şuayb Şefik; Jang, Hojin; Boix, Xavier; Cooper, Avi; Groth, Matt J; Sinha, Pawan
Humans possess impressive skills for recognizing faces even when the viewing conditions are challenging, such as long ranges, non-frontal regard, variable lighting, and atmospheric turbulence. We sought to characterize the effects of such viewing conditions on the face recognition performance of humans, and compared the results to those of DNNs. In an online verification task study, we used a 100 identity face database, with images captured at five different distances (2m, 5m, 300m, 650m and 1000m) three pitch values (00 - straight ahead, +/- 30 degrees) and three levels of yaw (00, 45, and 90 degrees). Participants were presented with 175 trials (5 distances x 7 yaw and pitch combinations, with 5 repetitions). Each trial included a query image, from a certain combination of range x yaw x pitch, and five options, all frontal short range (2m) faces. One was of the same identity as the query, and the rest were the most similar identities, chosen according to a DNN-derived similarity matrix. Participants ranked the top three most similar target images to the query image. The collected data reveal the functional relationship between human performance and multiple viewing parameters. Nine state-of-the-art pre-trained DNNs were tested for their face recognition performance on precisely the same stimulus set. Strikingly, DNN performance was significantly diminished by variations in ranges and rotated viewpoints. Even the best-performing network reported below 65% accuracy at the closest distance with a profile view of faces, with results dropping to near chance for longer ranges. The confusion matrices of DNNs were generally consistent across the networks, indicating systematic errors induced by viewing parameters. Taken together, these data not only help characterize human performance as a function of key ecologically important viewing parameters, but also enable a direct comparison of humans and DNNs in this parameter regime
Citation - Scopus: 7
The Use of Neurometric and Biometric Research Methods in Understanding the User Experience During Product Search of First-Time Buyers in E-Commerce - Conference Paper
(Springer, 2017) Rızvanoğlu, Kerem; Gürvardar, İrfan; Çakar, Tuna; Öztürk, Özgürol; Zengin Çelik, Deniz
Understanding user experience (UX) during e-commerce has been a relatively important research area especially in the last decade. The use of conventional methods in UX such as task-observation, in-depth interviews and questionnaires has already contributed for the measurement of the efficiency and effectiveness. This empirical study has aimed to make use of both conventional and neuroscientific methods simultaneously to provide a richer analysis framework for understanding the product search experience of the first-time buyers. The current work provides insights for the results from the combined use of conventional and neuroscientific-biometric methods in a UX study. Although this has been an exploratory study within a limited literature, the obtained results indicate a potential use of these methods for UX research, which may contribute to improve the relevant experience in various digital platforms.
Citation - WoS: 2
Citation - Scopus: 2
Facial Expression Recognition From Still Images
(2017) Gökmen, Muhittin; Gazioglu, Bilge Suheyla Akkoca
With the development of technology, Facial Expression Recognition (FER) become one of the important research areas in Human Computer Interaction. Changes in the movement of some muscles in face create the facial expressions. By defining these changes, facial expressions can be recognized. In this study, a cascaded structure consists of Local Zernike Moments (LZM), Local XOR Patterns (LXP) and Global Zernike Moments (GZM) methods is proposed for the FER problem. The generally used database is the Extended Chon - Kanade (CK +) in FER problems. The database consists of image sequences of 327 expressions of 118 people. Most FER system includes recognition of 7 classes of emotions happiness, sadness, surprise, anger, disgust, fear and contempt, and we use Library of Support Vector Machines (LIBSVM) classifier for multi class classification with the leave one out cross-validation method. Our overall system performance is measured as 90.34% for FER.
What Is the Effective Resolution of the Retinal Image of a Distant Face?
(Vision Sciences Society Annual Meeting Abstract, 2023) Arslan , Şuayb Şefik; Fux, Michal; Sinha, Pawan
We consider the following question: What is the effective resolution of a face image projected on the retina, when the face is at a specified distance from the eye? Though simple to state, this is a surprisingly challenging issue to resolve. The mapping between viewing distance and effective resolution cannot be readily derived based on the contrast sensitivity, Snellen acuity, or even the packing density of photoreceptors in the fovea. With initial guidelines derived from theoretical considerations, images of varying resolution were presented across a range of viewing distances. For each distance, participants were required to perform an ‘odd one out’ task. This involved detecting the one that was different from the rest in a 2x2 grid, with image resolution being the only dimension of variation. As the experiment progressed, the viewing distance decreased monotonically, and participants were able to detect increasingly subtle resolution differences between the three standard images and the outlier. The collected data have allowed us to establish the upper/lower bounds on the effective available resolution for typical human vision as a function of viewing distance. Interestingly, we find that humans perform significantly better, particularly at short ranges, than what a theoretical model predicts based on projected image size, cone density, and foveal extent. Accordingly, we suggest that the non-uniform in-fovea density, as well as less sharp fall-off in the acuity density function outside the fovea, need to be integrated into future theoretical models to translate viewing distance to perceived image characteristics. A pragmatic benefit of the mapping is that it enables a direct comparison of human face recognition performance as assessed across blur and viewing distance. Additionally, it allows us to systematically compare human performance on face recognition at varying distances with that of machine vision systems using the common axis of resolution.
Citation - Scopus: 4
Classification of Skin Lesion Images With Deep Learning Approaches
(University of Latvia, 2022) Kulavuz, Bahadır; Ertuğrul, Berkay; Bakırman, Tolga; Çakar, Tuna; Doğan, Metehan; Bayram, Bülent; Bayram, Buket
Skin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient's recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification. © 2022 Baltic Journal of Modern Computing. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 5
Private Minutia-Based Fingerprint Matching
(2015) Sarıer, Neyire Deniz
In this paper, we propose an efficient biometric authentication protocol for fingerprints particularly suited for the minutia-based representation. The novelty of the protocol is that we integrate the most efficient (linear complexity) private set intersection cardinality protocol of Cristofaro et al. and a suitable helper data system for biometrics in order to improve the accuracy of the system. We analyze the security of our scheme in the standard model based on well-exploited assumptions, considering malicious parties, which is essential to eliminate specific attacks on biometric authentication schemes designed for semi-honest adversaries only. Finally, the complexity is compared to the existing provably secure schemes for fingerprint matching, which shows that the new proposal outperforms them both in semi-honest and malicious security models.
Citation - Scopus: 1
Improved Bounds on the Moments of Guessing Cost
(IEEE, 2022) Arslan, Suayb S.; Haytaoglu, Elif
Guessing a random variable with finite or countably infinite support in which each selection leads to a positive cost value has recently been studied within the context of "guessing cost". In those studies, similar to standard guesswork, upper and lower bounds for the rho-th moment of guessing cost are described in terms of the known measure Renyi's entropy. In this study, we non-trivially improve the known bounds using previous techniques along with new notions such as balancing cost. We have demonstrated that the novel lower bound proposed in this work, achieves 5.84%, 18.47% higher values than that of the known lower bound for rho = 1 and rho = 5, respectively. As for the upper bound, the novel expression provides 10.93%, 5.54% lower values than that of the previously presented bounds for rho = 1 and rho = 5, respectively.
Citation - WoS: 1
Citation - Scopus: 2
Extracting, Computing, Coordination: What Does a Triphasic Erp Pattern Say About Language Processing?
(Elsevier, 2021) Çakar, Tuna; Eken, Aykut; Cedden, Gülay
The current study aims at contributing to the interpretation of the most prominent language-related ERP effects, N400 and P600, by investigating how neural responses to congruent and incongruent sentence endings vary, when the language processor processes the full array of the lexico-syntactic content in verbs with three affixes in canonical Turkish sentences. The ERP signals in response to three different violation conditions reveal a similar triphasic (P200/N400/P600) pattern resembling in topography and peak amplitude The P200 wave is interpreted as the extraction of meaning from written.form by generating a code which triggers the computation of neuronal ensembles in the distributed LTM (N400). The P600 potential reflects the widely distributed coordination process of activated neuronal patterns of semantic and morphosyntactic cues by connecting the generated subsets of these patterns and adapting them into the current context. It further can be deduced that these ERP components reflect cognitive rather than linguistic processes. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Citation - WoS: 1
Citation - Scopus: 1
Base Station-Assisted Cooperative Network Coding for Cellular Systems With Link Constraints
(IEEE, 2022) Arslan, Suayb S.; Pourmandi, Massoud; Haytaoglu, Elif
We consider a novel distributed data storage/caching scenario in a cellular network, where multiple nodes may fail/depart simultaneously To meet reliability, we allow cooperative regeneration of lost nodes with the help of base stations allocated in a set of hierarchical layers1. Due to this layered structure, a symbol download from each base station has a different cost, while the link capacities between the nodes of the cellular system and the base stations are also constrained. Under such a setting, we formulate the fundamental trade-off with closed form expressions between repair bandwidth cost and the storage space per node. Particularly, the minimum storage as well as bandwidth cost points are formulated. Finally, we provide an explicit optimal code construction for the minimum storage regeneration point for a special set of system parameters.
Citation - WoS: 4
Citation - Scopus: 4
Cost of Guessing: Applications To Data Repair
(Institute of Electrical and Electronics Engineers Inc., 2020) Arslan, Şuayb Şefik; Haytaoğlu, Elif
In this paper, we introduce the notion of cost of guessing and provide an optimal strategy for guessing a random variable taking values on a finite set whereby each choice may be associated with a positive finite cost value. Moreover, we drive asymptotically tight upper and lower bounds on the moments of cost of guessing problem. Similar to previous studies on the standard guesswork, established bounds on moments quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of the Rényi's entropy. A new random variable is introduced to bridge between cost of guessing and the standard guesswork and establish the guessing cost exponent on the moments of the optimal guessing. Furthermore, these bounds are shown to serve quite useful for finding repair latency cost for distributed data storage in which sparse graph codes may be utilized.

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