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  1. Book ; Online: Face Recognition Using Synthetic Face Data

    Granoviter, Omer / Gruzdev, Alexey / Loginov, Vladimir / Kogan, Max / Zvitia, Orly

    2023  

    Abstract: In the field of deep learning applied to face recognition, securing large-scale, high-quality datasets is vital for attaining precise and reliable results. However, amassing significant volumes of high-quality real data faces hurdles such as time ... ...

    Abstract In the field of deep learning applied to face recognition, securing large-scale, high-quality datasets is vital for attaining precise and reliable results. However, amassing significant volumes of high-quality real data faces hurdles such as time limitations, financial burdens, and privacy issues. Furthermore, prevalent datasets are often impaired by racial biases and annotation inaccuracies. In this paper, we underscore the promising application of synthetic data, generated through rendering digital faces via our computer graphics pipeline, in achieving competitive results with the state-of-the-art on synthetic data across multiple benchmark datasets. By finetuning the model,we obtain results that rival those achieved when training with hundreds of thousands of real images (98.7% on LFW [1]). We further investigate the contribution of adding intra-class variance factors (e.g., makeup, accessories, haircuts) on model performance. Finally, we reveal the sensitivity of pre-trained face recognition models to alternating specific parts of the face by leveraging the granular control capability in our platform.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-05-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  2. Book ; Online: Hands-Up

    Yudkin, Paul / Friedman, Eli / Zvitia, Orly / Elbaz, Gil

    Leveraging Synthetic Data for Hands-On-Wheel Detection

    2022  

    Abstract: Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and physics-based ray-tracing rendering in order to represent the world in 3D ... ...

    Abstract Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and physics-based ray-tracing rendering in order to represent the world in 3D and create highly realistic images. Datagen has specialized in the generation of high-quality 3D humans, realistic 3D environments and generation of realistic human motion. This technology has been developed into a data generation platform which we used for these experiments. This work demonstrates the use of synthetic photo-realistic in-cabin data to train a Driver Monitoring System that uses a lightweight neural network to detect whether the driver's hands are on the wheel. We demonstrate that when only a small amount of real data is available, synthetic data can be a simple way to boost performance. Moreover, we adopt the data-centric approach and show how performing error analysis and generating the missing edge-cases in our platform boosts performance. This showcases the ability of human-centric synthetic data to generalize well to the real world, and help train algorithms in computer vision settings where data from the target domain is scarce or hard to collect.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-05-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  3. Book ; Online: Knowing the Distance

    Friedman, Eli / Lehr, Assaf / Gruzdev, Alexey / Loginov, Vladimir / Kogan, Max / Rubin, Moran / Zvitia, Orly

    Understanding the Gap Between Synthetic and Real Data For Face Parsing

    2023  

    Abstract: The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-modality labels. Although recent studies have demonstrated impressive ... ...

    Abstract The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-modality labels. Although recent studies have demonstrated impressive results when training networks solely on synthetic data, there remains a performance gap between synthetic and real data that is commonly attributed to lack of photorealism. The aim of this study is to investigate the gap in greater detail for the face parsing task. We differentiate between three types of gaps: distribution gap, label gap, and photorealism gap. Our findings show that the distribution gap is the largest contributor to the performance gap, accounting for over 50% of the gap. By addressing this gap and accounting for the labels gap, we demonstrate that a model trained on synthetic data achieves comparable results to one trained on a similar amount of real data. This suggests that synthetic data is a viable alternative to real data, especially when real data is limited or difficult to obtain. Our study highlights the importance of content diversity in synthetic datasets and challenges the notion that the photorealism gap is the most critical factor affecting the performance of computer vision models trained on synthetic data.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-03-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: Co-registration of white matter tractographies by adaptive-mean-shift and Gaussian mixture modeling.

    Zvitia, Orly / Mayer, Arnaldo / Shadmi, Ran / Miron, Shmuel / Greenspan, Hayit K

    IEEE transactions on medical imaging

    2010  Volume 29, Issue 1, Page(s) 132–145

    Abstract: In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their ...

    Abstract In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two GMMs and is performed by maximizing their correlation ratio. A nine-parameters affine transform is recovered and eventually refined to a twelve-parameters affine transform using an innovative mean-shift based registration refinement scheme presented in this paper. The validation of the algorithm on synthetic intrasubject data demonstrates its robustness to interrupted and deviating fiber artifacts as well as outliers. Using real intrasubject data, a comparison is conducted to other intensity based and fiber-based registration algorithms, demonstrating competitive results. An option for tracking-in-time, on specific white matter fiber tracts, is also demonstrated on the real data.
    MeSH term(s) Algorithms ; Brain/anatomy & histology ; Cluster Analysis ; Diffusion Tensor Imaging/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Models, Neurological ; Normal Distribution ; Reproducibility of Results
    Language English
    Publishing date 2010-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2009.2029097
    Database MEDical Literature Analysis and Retrieval System OnLINE

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