In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection

M. F. Karaaba, O. Surinta, L. R. B. Schomaker, M. A. Wiering


In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann machine and the histogram of oriented gradients, are compared to extract feature vectors from a sliding window. Then a support vector machine is used to accurately localize the eyes. After the eye coordinates are obtained through our eye detector, the in-plane angle is estimated by calculating the arc-tangent of horizontal and vertical parts of the distance between left and right eye center points. By using this calculated in-plane angle, the face is subsequently rotationally aligned. We tested our approach on three different face datasets: IMM, Labeled Faces in the Wild (LFW) and FERET. Moreover, to compare the effect of rotational aligning on face recognition performance, we performed experiments using a face recognition method using rotationally aligned and non-aligned face images from the IMM dataset. The results show that our method calculates the in-plane rotation angle with high precision and this leads to a significant gain in face recognition performance.


  1. Anvar, S., Yau, W.-Y., Nandakumar, K., and Teoh, E. K. (2013). Estimating In-Plane Rotation Angle for Face Images from Multi-Poses. In Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on, pages 52-57.
  2. Arróspide, J., Salgado, L., and Camplani, M. (2013). Image-based on-road vehicle detection using costeffective histograms of oriented gradients. Journal of Visual Communication and Image Representation, 24(7):1182-1190.
  3. Castrillón-Santana, M., Déniz-Suárez, O., Antón-Canalís, L., and Lorenzo-Navarro, J. (2008). Face and Facial Feature Detection Evaluation. In Third International Conference on Computer Vision Theory and Applications, VISAPP08, pages 167-172.
  4. Cootes, T. F., Edwards, G. J., and Taylor, C. J. (1998). Active Appearance Models. In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 484-498. Springer.
  5. Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J. (1995). Active shape models - their training and application. Computer Vision and Image Understanding, 61(1):38-59.
  6. Dahmane, M. and Meunier, J. (2011). Emotion recognition using dynamic grid-based HoG features. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on, pages 884- 888.
  7. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893.
  8. Déniz, O., Bueno, G., Salido, J., and la Torre, F. D. (2011). Face recognition using histograms of oriented gradients. Pattern Recognition Letters, 32(12):1598-1603.
  9. Hansen, D. W. and Ji, Q. (2010). In the Eye of the Beholder: A Survey of Models for Eyes and Gaze. IEEE Transactions on Pattern Analysis & Machine Intelligence, 32(3):478-500.
  10. Hasan, M. K. and Pal, C. J. (2011). Improving Alignment of Faces for Recognition. In Robotic and Sensors Environments, pages 249-254. IEEE.
  11. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800.
  12. Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst.
  13. Karaaba, M. F., Wiering, M. A., and Schomaker, L. (2014). Machine Learning for Multi-View Eye-Pair Detection. Engineering Applications of Artificial Intelligence, 33(0):69 - 79.
  14. Li, H., Wang, P., and Shen, C. (2010). Robust face recognition via accurate face alignment and sparse representation. In Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on, pages 262-269.
  15. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60:91-110.
  16. Monzo, D., Albiol, A., Sastre, J., and Albiol, A. (2011). Precise eye localization using HOG descriptors. Machine Vision and Applications, 22(3):471-480.
  17. Nordstrøm, M. M., Larsen, M., Sierakowski, J., and Stegmann, M. B. (2004). The imm face database - an annotated dataset of 240 face images. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, DTU.
  18. Phillips, P. J., Wechsler, H., Huang, J., and Rauss, P. (1998). The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing, 16(5):295-306.
  19. Song, F., Tan, X., Chen, S., and Zhou, Z.-H. (2013). A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recognition, 46(12):3157 - 3173.
  20. Vapnik, V. (1998). Statistical learning theory. Wiley.
  21. Wang, H., Klaser, A., Schmid, C., and Liu, C.-L. (2011). Action recognition by dense trajectories. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 3169-3176.
  22. Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2879-2886.

Paper Citation

in Harvard Style

Karaaba M., Surinta O., Schomaker L. and Wiering M. (2015). In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 392-399. DOI: 10.5220/0005308303920399

in Bibtex Style

author={M. F. Karaaba and O. Surinta and L. R. B. Schomaker and M. A. Wiering},
title={In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},

in EndNote Style

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection
SN - 978-989-758-090-1
AU - Karaaba M.
AU - Surinta O.
AU - Schomaker L.
AU - Wiering M.
PY - 2015
SP - 392
EP - 399
DO - 10.5220/0005308303920399