2023 |
Hrovatič, Anja; Peer, Peter; Štruc, Vitomir; Emeršič, Žiga Efficient ear alignment using a two-stack hourglass network Članek v strokovni reviji V: IET Biometrics , str. 1-14, 2023, ISSN: 2047-4938. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, deep learning, ear, ear alignment, ear recognition @article{UhljiIETZiga, Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under-explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two-step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two-Stack Hourglass model (2-SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre-defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2-SHGNet model leads to more accurate landmark predictions than competing state-of-the-art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery. |
2016 |
Ribič, Metod; Emeršič, Žiga; Štruc, Vitomir; Peer, Peter Influence of alignment on ear recognition : case study on AWE Dataset Proceedings Article V: Proceedings of the Electrotechnical and Computer Science Conference (ERK), str. 131-134, Portorož, Slovenia, 2016. Povzetek | Povezava | BibTeX | Oznake: AWE, AWE dataset, biometrics, ear alignment, ear recognition, image alignment, Ransac, SIFT @inproceedings{RibicERK2016, Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. Recently, we presented Annotated Web Ears dataset that opens new challenges in ear recognition. In this paper we test the influence of alignment on recognition performance and prove that even with the alignment the database is still very challenging, even-though the recognition rate is improved due to alignment. We also prove that more sophisticated alignment methods are needed to address the AWE dataset efficiently |
Objave
2023 |
Efficient ear alignment using a two-stack hourglass network Članek v strokovni reviji V: IET Biometrics , str. 1-14, 2023, ISSN: 2047-4938. |
2016 |
Influence of alignment on ear recognition : case study on AWE Dataset Proceedings Article V: Proceedings of the Electrotechnical and Computer Science Conference (ERK), str. 131-134, Portorož, Slovenia, 2016. |