Abstract
Human face landmark detection algorithms have numerous applications. Current face landmark detection algorithms limit themselves to features around eyes, nose, cheeks and lips. Face landmark detection combined with augmented reality technology has given rise to commercially popular virtual try-on applications. To realize use cases of virtual jewelry try-on like earrings on smartphones, landmark points of human ear is required, but this field is not much explored in the literature. Existing methods are not accurate enough in different face poses and lighting conditions. Proposed method offers solution for ear landmark detection considering the computational requirements of mobility devices, and comprises ear localization followed by ear landmark detection. It adopts Haar cascade based model for ear localization and an Ensemble of Regression Trees for ear landmark detection. The experimental results and comparison with state-of-the-art methods show that the proposed method accurately localizes the ear, provides correct landmark points and is fast enough to run on mobility devices with low memory footprints. Comparison with popular methods shows the novelty points in the proposed approach.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1867–1874 (2014)
Pflug, A., Busch, C.: Ear biometrics: a survey of detection, feature extraction and recognition methods. IET Biometrics 1(2), 114–129 (2012)
Abaza, A., Hebert, C., Harrison, M.: Fast learning ear detection for real-time surveillance. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems, pp. 1–6 (2010)
Islam, S., Davies, R., Bennamoun, M., Mian, A.: Efficient detection and recognition of 3D ears. Int. J. Comput. Vision 95, 52–73 (2011)
Emersic, Z., Struc, V., Peer, P.: Ear recognition: more than a survey. Neurocomputing 255, 26–39 (2017)
Hansley, E.E., Segundo, M.P., Sarkar, S.: Employing fusion of learned and handcrafted features for unconstrained ear recognition. IET Biometrics 7(3), 215–223 (2017)
Zhou, Y., Zaferiou, S.: Deformable models of ears in-the-wild for alignment and recognition. In: 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp 626–633 (2017)
Ravindran, S.: Ear Contour Detection and Modeling Using Statistical Shape Models (2014). https://tigerprints.clemson.edu/all_theses/1992
CaratLane. https://www.caratlane.com/virtual-try-on/
Yan, P., Bowyer, K.W.: Biometric recognition using three-dimensional ear shape. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1297–1308 (2007)
Kumar, A., Wu, C.: Automated human identification using ear imaging. Pattern Recogn. 45(3), 956–968 (2011)
FacePlusPlus. https://api-us.faceplusplus.com/facepp/v3/detect
YouCam Makeup Application. https://play.google.com/store/apps/details?id=com.cyberlink.youcammakeup&hl=en_IN
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, H., Goel, S., Sharma, R., Kalose Mathsyendranath, R. (2020). Real-Time Ear Landmark Detection Using Ensemble of Regression Trees. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_36
Download citation
DOI: https://doi.org/10.1007/978-981-15-4018-9_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4017-2
Online ISBN: 978-981-15-4018-9
eBook Packages: Computer ScienceComputer Science (R0)