1. About

    I am a PhD candidate at LINKMEDIA team in INRIA Rennes and InterDigital. I am working on Semantic Multimodal Question Answering working with Yannis Avrithis. I have received my BS and MS degrees from Electronics Engineering at Istanbul Technical University.



  1. News



Research

  1. See Google Scholar for all publications.

    Offline Signature Verification on Real-World Documents
    Deniz Engin, Alperen Kantarcı, Seçil Arslan, Hazım Kemal Ekenel
    CVPR Biometrics Workshop, 2020
    arxiv | abstract

    Research on offline signature verification has explored a large variety of methods on multiple signature datasets, which are collected under controlled conditions. However, these datasets may not fully reflect the characteristics of the signatures in some practical use cases. Real-world signatures extracted from the formal documents may contain different types of occlusions, for example, stamps, company seals, ruling lines, and signature boxes. Moreover, they may have very high intra-class variations, where even genuine signatures resemble forgeries. In this paper, we address a real-world writer independent offline signature verification problem, in which, a bank’s customers’ transaction request documents that contain their occluded signatures are compared with their clean reference signatures. Our proposed method consists of two main components, a stamp cleaning method based on CycleGAN and signature representation based on CNNs. We extensively evaluate different verification setups, fine-tuning strategies, and signature representation approaches to have a thorough analysis of the problem. Moreover, we conduct a human evaluation to show the challenging nature of the problem. We run experiments both on our custom dataset, as well as on the publicly available Tobacco-800 dataset. The experimental results validate the difficulty of offline signature verification on real-world documents. However, by employing the stamp cleaning process, we improve the signature verification performance significantly.

  2. Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
    Deniz Engin, Anıl Genç, Hazım Kemal Ekenel
    CVPR NTIRE Workshop, 2018
    arxiv | code | abstract

    In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner. Moreover, the proposed approach does not rely on estimation of the atmospheric scattering model parameters. Our method enhances CycleGAN formulation by combining cycle-consistency and perceptual losses in order to improve the quality of textural information recovery and generate visually better haze-free images. Typically, deep learning models for dehazing take low resolution images as input and produce low resolution outputs. However, in the NTIRE 2018 challenge on single image dehazing, high resolution images were provided. Therefore, we apply bicubic downscaling. After obtaining low-resolution outputs from the network, we utilize the Laplacian pyramid to upscale the output images to the original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE datasets. Extensive experiments demonstrate that the proposed approach improves CycleGAN method both quantitatively and qualitatively.


  3. Face Frontalization for Cross-Pose Facial Expression Recognition
    Deniz Engin, Christophe Ecabert, Hazım Kemal Ekenel, Jean-Philippe Thiran
    EUSIPCO, 2018
    abstract

    In this paper, we have explored the effect of pose normalization for cross-pose facial expression recognition. We have first presented an expression preserving face frontalization method. After face frontalization step, for facial expression representation and classification, we have employed both a traditional approach, by using hand-crafted features, namely local binary patterns, in combination with support vector machine classification and a relatively more recent approach based on convolutional neural networks. To evaluate the impact of face frontalization on facial expression recognition performance, we have conducted cross-pose, subject-independent expression recognition experiments using the BU3DFE database. Experimental results show that pose normalization improves the performance for cross-pose facial expression recognition. Especially, when local binary patterns in combination with support vector machine classifier is used, since this facial expression representation and classification does not handle pose variations, the obtained performance increase is significant. Convolutional neural networks-based approach is found to be more successful handling pose variations, when it is fine-tuned on a dataset that contains face images with varying pose angles. Its performance is further enhanced by benefiting from face frontalization.