2021 |
Emeršič, Žiga; Sušanj, Diego; Meden, Blaž; Peer, Peter; Štruc, Vitomir ContexedNet : Context-Aware Ear Detection in Unconstrained Settings Journal Article In: IEEE Access, pp. 1–17, 2021, ISSN: 2169-3536. Abstract | Links | BibTeX | Tags: biometrics, contextual information, deep leraning, ear detection, ear recognition, ear segmentation, neural networks, segmentation @article{ContexedNet_Emersic_2021,Ear detection represents one of the key components of contemporary ear recognition systems. While significant progress has been made in the area of ear detection over recent years, most of the improvements are direct results of advances in the field of visual object detection. Only a limited number of techniques presented in the literature are domain--specific and designed explicitly with ear detection in mind. In this paper, we aim to address this gap and present a novel detection approach that does not rely only on general ear (object) appearance, but also exploits contextual information, i.e., face--part locations, to ensure accurate and robust ear detection with images captured in a wide variety of imaging conditions. The proposed approach is based on a Context--aware Ear Detection Network (ContexedNet) and poses ear detection as a semantic image segmentation problem. ContexedNet consists of two processing paths: 1) a context--provider that extracts probability maps corresponding to the locations of facial parts from the input image, and 2) a dedicated ear segmentation model that integrates the computed probability maps into a context--aware segmentation-based ear detection procedure. ContexedNet is evaluated in rigorous experiments on the AWE and UBEAR datasets and shown to ensure competitive performance when evaluated against state--of--the--art ear detection models from the literature. Additionally, because the proposed contextualization is model agnostic, it can also be utilized with other ear detection techniques to improve performance. |
Pevec, Klemen; Grm, Klemen; Štruc, Vitomir Benchmarking Crowd-Counting Techniques across Image Characteristics Journal Article In: Elektorethniski Vestnik, vol. 88, iss. 5, pp. 227-235, 2021. Abstract | Links | BibTeX | Tags: CNN, crowd counting, drones, image characteristics, model comparison, neural networks @article{CrowdCountingPevec,Crowd--counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd--counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd--counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd--counting methods can provide accurate and usable results. |
2020 |
Stepec, Dejan; Emersic, Ziga; Peer, Peter; Struc, Vitomir Constellation-Based Deep Ear Recognition Book Section In: Jiang, R.; Li, CT.; Crookes, D.; Meng, W.; Rosenberger, C. (Ed.): Deep Biometrics: Unsupervised and Semi-Supervised Learning, Springer, 2020, ISBN: 978-3-030-32582-4. Abstract | Links | BibTeX | Tags: biometrics, CNN, deep learning, ear recognition, neural networks @incollection{Stepec2020COMEar,This chapter introduces COM-Ear, a deep constellation model for ear recognition. Different from competing solutions, COM-Ear encodes global as well as local characteristics of ear images and generates descriptive ear representations that ensure competitive recognition performance. The model is designed as dual-path convolutional neural network (CNN), where one path processes the input in a holistic manner, and the second captures local images characteristics from image patches sampled from the input image. A novel pooling operation, called patch-relevant-information pooling, is also proposed and integrated into the COM-Ear model. The pooling operation helps to select features from the input patches that are locally important and to focus the attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx). |