Abstract
Face recognition is a complex task due to the challenges of varying pose, illumination, scaling, rotation, and occlusion in live video feed. This paper proposes a hybrid approach for face recognition in video called Deformable Parts Model with Scale Invariant Feature Transform (DPSIFT), to make face recognition system invariant to illumination, scaling, rotation, and limited pose. The proposed method identifies the significant points of the face using deformable part model and SIFT feature descriptors are extracted for those significant points. Fast Approximate Nearest Neighbor (FLANN) algorithm is used to match the SIFT descriptors between gallery image and probe image to recognize the face. The proposed method is tested with video datasets like YouTube celebrities, FJU, and MIT-India. DPSIFT method was found to perform better than the existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harris, C., Stephens, M.J.: A combined corner and edge detector. In: Alvey Vision Conference, 147–152 (1988)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. (2004)
Bay, H., Ess, A., Tuytelaars, Tinne, Van Gool, Luc: Speeded up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), (2004)
Cootes, T., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Beumer, G., Tao, Q., Bazen, A., Veldhuis, R.: A landmark paper in face recognition. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR-2006). IEEE Computer Society Press (2006)
Cristinacce, D., Cootes, T.: Facial feature detection using adaboost with shape constraints. In: 14th Proceedings British Machine Vision Conference (BMVC-2003), 231–240 (2003)
Erukhimov, V., Lee, K.: A bottom-up framework for robust facial feature detection. In: 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG2008), 1–6 (2008)
Cristinacce, D., Cootes, T., Scott, I.: A multistage approach to facial feature detection. In: 15th British Machine Vision Conference (BMVC-2004), 277–286 (2004)
Uricar, M., Franc, V., Hlavac, V.: Detector of facial landmarks learned by the structured output SVM, VISAPP ‘12: Proceedings of the 7th International Conference on Computer Vision Theory and Applications (2012)
Uricar, M.: Detector of facial landmarks, Master’s Thesis, supervised by V. Franc (2011)
Acknowledgment
The authors thank BRNS-BARC for support in the successful completion of the project work titled “Face Recognition System using Video Analytics.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this chapter
Cite this chapter
Mohanraj, V., Vaidehi, V., Kumar, R., Nakkeeran, R. (2016). Face Recognition in Video Using Deformable Parts Model with Scale Invariant Feature Transform (DPSIFT). In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2653-6_5
Download citation
DOI: https://doi.org/10.1007/978-81-322-2653-6_5
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2651-2
Online ISBN: 978-81-322-2653-6
eBook Packages: EngineeringEngineering (R0)