KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network

In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201

Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

Frame-level visual features are generally aggregated in time with the techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust video-level representation. We here introduce a learnable aggregation technique whose primary objective is to retain short-time temporal structure between frame-level features and their spatial interdependencies in the representation. Also, it can be easily adapted to the cases where there have very scarce training samples. We evaluate the method on a real-fake expression prediction dataset to demonstrate its superiority. Our method obtains 65% score on the test dataset in the official MAP evaluation and there is only one misclassified decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) . Lastly, we believe that this method can be extended to different problems such as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop

Hiding Data and Detecting Hidden Data in Raw Video Components Using SIFT Points

Steganography is a science of hiding data in a medium whereas steganalysis is composed of attacks to find the hidden data in a cover medium. Since hiding data in a text file would disturb the coherence of the text or make it suspicious, systematically changing pixels of a visual is a more common method. This process is performed on pixels that are spatially (and/or temporally, for video components) distant from each other so that a viewer\u27s eye can be deceived. Online media are subject to modification such as compression, resolution change, visual modifications, and such which makes Scale Invariant Feature Transform (SIFT) points appropriate candidates for steganography. The current paper has two aims: the first is to propose a method that uses the SIFT points of a video for steganography. The second aim is to use Convolutional Neural Networks (CNN) as a steganalysis tool to detect the suspicious pixels of a video. The results indicate that the proposed steganography method is effective because it yields higher peak signal-to-noise ratio (PSNR = 95.41 dB) compared to other techniques described in cybersecurity literature, and CNN cannot detect hidden data with much success due to its 52% accuracy rate

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors, and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so-called EndNet, that is based on a two-staged autoencoder network. This well-known structure is completely enhanced and restructured by introducing additional layers and a projection metric [i.e., spectral angle distance (SAD) instead of inner product] to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers, such as nonlinearity and sparsity for autoencoder networks. Finally, due to the stochastic-gradient-based approach, the method is scalable for large-scale data and it can be accelerated on graphical processing units. To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known data sets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in the literature

The Relationship Between Dietary Intakes and Total Kidney Volume in Patients with Autosomal Dominant Polycystic Kidney Disease Dietary Intake and Polycystic Kidney Volume

Aim: There is a need to understand autosomal dominant polycystic kidney disease (ADPKD) patients’ dietary habits since dietary interventions may have potential effects on ADPKD. In this study, we aimed to analyze the relationship between dietary nutrient intake and total kidney volume (TKV). Methods: This cross-sectional study was conducted on 54 ADPKD patients recruited from the Nephrology outpatient clinic between June and July 2014. TKV was determined by magnetic-resonance imaging and general characteristics, biochemical and urinary parameters were determined. The nutrient intakes of patients were calculated using the three-day dietary records obtained on three consecutive days. Results: The total kidney-volume median was found to be 1407 mL. Patients’ total dietary energy and protein intakes were 25.8±9.4 kcal/kg, 0.9±0.3 g/kg, respectively. The percentage of carbohydrates, protein, and fat in energy was 49±7%, 14±3%, 37±7%, respectively. The mean intakes of thiamin, riboflavin, B6, calcium, magnesium, and zinc were sufficient, the mean dietary potassium intake was insufficient; and sodium intake was excessive in both sexes. In females, there was a negative but weak correlation between dietary vitamin C intake and TKV. In males, a negative but weak correlation was found between TKV and dietary intake of fiber, water, vitamin B6, vitamin K, magnesium, and iron. Conclusions: Dietary micronutrient intake may affect TKV according to sex. © 2022, Galenos Publishing House. All rights reserved