Automatic Detection of Face in Video Sequences by using Extended Semi Local Binary Patterns

ITHAYA RANI PANNEERSELVAM, Hari Prasath T.

Article ID: 216
Vol 1, Issue 1, 2018

VIEWS - 128 (Abstract) 14 (PDF)

Abstract


Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

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References


1. M.Yang, D. Kriegman, N. Ahuja , Detecting faces in images: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 2002, 34–58.

2. Hielm, E., Low, B.: `Face detection: a survey`, Computer Vision and Image Understanding, 2001, 83, (3), 236–274.

3. Borah, S. Konwar, S. ; Tuithung, T. ; Rathi, R. A human face detection method based on connected component analysis , Communications and Signal Processing (ICCSP), 2014 International Conference on 1205 – 1208,2014.

4. Byung-Hun Oh , Kwang-Seok Hong , A study on facial components detection method for face-based emotion recognition , International Conference on Audio, Language and Image Processing (ICALIP), 2014 , 256 – 259

5. H. Sagha, S. Kasaei, E. Enayati, M. Dehghani , Finding Sparse Features in Face Detection Using Genetic Algorithms , IEEE International Conference on Computational Cybernetics, ICCC 2008 ,179 - 182

6. Li Xiaohua , Kin-Man Lam , Shen Lansun , Zhou Jiliu Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers , Pattern Recognition Letters , (2009) 717–728

7. Lin-Lin Huang *, Akinobu Shimizu, Hidefumi Kobatake, Robust face detection using Gabor filter features , Pattern Recognition Letters 26 (2005) 1641–1649

8. T. Yun and L. Guan, “Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques,” Pattern Recognit., vol. 42, no. 9, pp. 1859–1868, Sep. 2009.

9. T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on features distributions,” Patt. R ecognit., vol. 29, no. 1, pp. 51–59, 1996.

10. B. Froba and A. Ernst, “Face detection with the modified census transform,”in Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2004, pp. 91–96.

11. B. Jun and D. Kim, “Robust face detection using local gradient patterns and evidence accumulation,” Patt. Recognit., vol. 45, no. 9, pp. 3304–3316, 2012.

12. Donghoon Kim, Rozenn Dahyot, Face Components Detection using SURF Descriptors and SVMs , Machine Vision and Image Processing International Conference, 2008. 51 – 56 .

13. Paul Viola and Michael Jones, “Robust real-time object detection,” IJCV, vol. 57, no. 2, pp. 137–154, 2004.

14. Kyungjoong Jeong, Jaesik Choi and Gil-Jin Jang , Semi-Local Structure Patterns for Robust Face Detection, IEEE Signal Processing Letters, VOL. 22, NO. 9, 2015 pp 1400-1403

15. Kim M, Kumar S, Pavlovic V, Rowley H (2008) Face Tracking and Recognition with Visual Constraints in Real-World Videos. IEEE Conf. Computer Vision and Pattern Recognition

16. P. Viola, M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001,Vol (1), pp.511–518.


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