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Dr. Adrian Sergiu Darabant
Dept. of Computer Science, Fac. of Mathematics and Computer Science, Babes Bolyai University, Cluj Napoca, Romania

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0 Augmented Reality
0 Deep Learning
0 Image Processing
0 Machine Learning
0 Convolutional Neural Networks

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Journal article
Published: 19 January 2021 in Mathematics
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The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities` images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

ACS Style

Adrian Sergiu Darabant; Diana Borza; Radu Danescu. Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study. Mathematics 2021, 9, 195 .

AMA Style

Adrian Sergiu Darabant, Diana Borza, Radu Danescu. Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study. Mathematics. 2021; 9 (2):195.

Chicago/Turabian Style

Adrian Sergiu Darabant; Diana Borza; Radu Danescu. 2021. "Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study." Mathematics 9, no. 2: 195.

Journal article
Published: 20 November 2020 in Sustainability
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Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle manually. Often, the algorithms and methods needed for solving these tasks are problem dependent. We propose an automatic method for creating new convolutional neural network architectures which are specifically designed to solve a given problem. We describe our method in detail and we explain its reduced carbon footprint, computation time and cost compared to a manual approach. Our method uses a rewarding mechanism for creating networks with good performance and so gradually improves its architecture proposals. The application for the algorithm that we chose for this paper is segmentation of eyeglasses from images, but our method is applicable, to a larger or lesser extent, to any image processing task. We present and discuss our results, including the architecture that obtained 0.9683 intersection-over-union (IOU) score on our most complex dataset.

ACS Style

Sergiu Nistor; Tudor Ileni; Adrian Dărăbant. Automatic Development of Deep Learning Architectures for Image Segmentation. Sustainability 2020, 12, 9707 .

AMA Style

Sergiu Nistor, Tudor Ileni, Adrian Dărăbant. Automatic Development of Deep Learning Architectures for Image Segmentation. Sustainability. 2020; 12 (22):9707.

Chicago/Turabian Style

Sergiu Nistor; Tudor Ileni; Adrian Dărăbant. 2020. "Automatic Development of Deep Learning Architectures for Image Segmentation." Sustainability 12, no. 22: 9707.

Journal article
Published: 14 December 2017 in Sensors
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Micro-expressions play an essential part in understanding non-verbal communication and deceit detection. They are involuntary, brief facial movements that are shown when a person is trying to conceal something. Automatic analysis of micro-expression is challenging due to their low amplitude and to their short duration (they occur as fast as 1/15 to 1/25 of a second). We propose a fully micro-expression analysis system consisting of a high-speed image acquisition setup and a software framework which can detect the frames when the micro-expressions occurred as well as determine the type of the emerged expression. The detection and classification methods use fast and simple motion descriptors based on absolute image differences. The recognition module it only involves the computation of several 2D Gaussian probabilities. The software framework was tested on two publicly available high speed micro-expression databases and the whole system was used to acquire new data. The experiments we performed show that our solution outperforms state of the art works which use more complex and computationally intensive descriptors.

ACS Style

Diana Borza; Radu Danescu; Razvan Itu; Adrian Sergiu Darabant. High-Speed Video System for Micro-Expression Detection and Recognition. Sensors 2017, 17, 2913 .

AMA Style

Diana Borza, Radu Danescu, Razvan Itu, Adrian Sergiu Darabant. High-Speed Video System for Micro-Expression Detection and Recognition. Sensors. 2017; 17 (12):2913.

Chicago/Turabian Style

Diana Borza; Radu Danescu; Razvan Itu; Adrian Sergiu Darabant. 2017. "High-Speed Video System for Micro-Expression Detection and Recognition." Sensors 17, no. 12: 2913.

Conference paper
Published: 13 October 2017 in Privacy Enhancing Technologies
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In this paper, we address the problem of skin tone classification in facial images, which has applications in various domains: visagisme, soft biometry and surveillance systems. We propose four skin tone classification algorithms and analyze their performance using different color spaces. The first two methods rely directly on pixel values, while the latter two divide the image into cells and classify the skin tone based on the color histograms of these cells. The proposed solutions were trained and evaluated on images from four publicly available databases and on images captured in our laboratory. The best accuracy (87.06%) is obtained using cell histograms of the Lab color space and support vector machine classifier.

ACS Style

Diana Borza; Sergiu Cosmin Nistor; Adrian Sergiu Darabant. Towards Automatic Skin Tone Classification in Facial Images. Privacy Enhancing Technologies 2017, 10485, 299 -309.

AMA Style

Diana Borza, Sergiu Cosmin Nistor, Adrian Sergiu Darabant. Towards Automatic Skin Tone Classification in Facial Images. Privacy Enhancing Technologies. 2017; 10485 ():299-309.

Chicago/Turabian Style

Diana Borza; Sergiu Cosmin Nistor; Adrian Sergiu Darabant. 2017. "Towards Automatic Skin Tone Classification in Facial Images." Privacy Enhancing Technologies 10485, no. : 299-309.

Conference paper
Published: 01 August 2017 in Computer Vision
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Large distributed databases are split into fragments stored on far distant nodes that communicate through a communication network. Query execution requires data transfers between the processing sites of the system. In this paper we propose a solution for minimizing raw data transfers by re-arranging and replicating existing data within the constraints of the original database architecture. The proposed method gathers incremental knowledge about data access patterns and database statistics to solve the following problem: online re-allocation of the fragments in order to constantly optimize the query response time. We model our solution as a transport network and show in the final section the experimental numerical results we obtain by comparing the improvements obtained between various database configurations, before and after optimization.

ACS Style

Adrian Sergiu Darabant; Leon Tambulea; Viorica Varga. Access Patterns Optimization in Distributed Databases Using Data Reallocation. Computer Vision 2017, 10438, 178 -186.

AMA Style

Adrian Sergiu Darabant, Leon Tambulea, Viorica Varga. Access Patterns Optimization in Distributed Databases Using Data Reallocation. Computer Vision. 2017; 10438 ():178-186.

Chicago/Turabian Style

Adrian Sergiu Darabant; Leon Tambulea; Viorica Varga. 2017. "Access Patterns Optimization in Distributed Databases Using Data Reallocation." Computer Vision 10438, no. : 178-186.

Journal article
Published: 16 July 2016 in Sensors
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The accurate extraction and measurement of eye features is crucial to a variety of domains, including human-computer interaction, biometry, and medical research. This paper presents a fast and accurate method for extracting multiple features around the eyes: the center of the pupil, the iris radius, and the external shape of the eye. These features are extracted using a multistage algorithm. On the first stage the pupil center is localized using a fast circular symmetry detector and the iris radius is computed using radial gradient projections, and on the second stage the external shape of the eye (of the eyelids) is determined through a Monte Carlo sampling framework based on both color and shape information. Extensive experiments performed on a different dataset demonstrate the effectiveness of our approach. In addition, this work provides eye annotation data for a publicly-available database.

ACS Style

Diana Borza; Adrian Sergiu Darabant; Radu Danescu. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors 2016, 16, 1105 .

AMA Style

Diana Borza, Adrian Sergiu Darabant, Radu Danescu. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors. 2016; 16 (7):1105.

Chicago/Turabian Style

Diana Borza; Adrian Sergiu Darabant; Radu Danescu. 2016. "Real-Time Detection and Measurement of Eye Features from Color Images." Sensors 16, no. 7: 1105.

Journal article
Published: 10 October 2013 in Sensors
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This paper presents a system that automatically extracts the position of the eyeglasses and the accurate shape and size of the frame lenses in facial images. The novelty brought by this paper consists in three key contributions. The first one is an original model for representing the shape of the eyeglasses lens, using Fourier descriptors. The second one is a method for generating the search space starting from a finite, relatively small number of representative lens shapes based on Fourier morphing. Finally, we propose an accurate lens contour extraction algorithm using a multi-stage Monte Carlo sampling technique. Multiple experiments demonstrate the effectiveness of our approach.

ACS Style

Diana Borza; Adrian Sergiu Darabant; Radu Danescu. Eyeglasses Lens Contour Extraction from Facial Images Using an Efficient Shape Description. Sensors 2013, 13, 13638 -13658.

AMA Style

Diana Borza, Adrian Sergiu Darabant, Radu Danescu. Eyeglasses Lens Contour Extraction from Facial Images Using an Efficient Shape Description. Sensors. 2013; 13 (10):13638-13658.

Chicago/Turabian Style

Diana Borza; Adrian Sergiu Darabant; Radu Danescu. 2013. "Eyeglasses Lens Contour Extraction from Facial Images Using an Efficient Shape Description." Sensors 13, no. 10: 13638-13658.