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Face detection with a Viola–Jones based hybrid network

Face detection with a Viola–Jones based hybrid network

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Face detection is the determination of the positions and sizes of faces, primarily human, within digital images and videos, often as a component of a broader facial recognition system. It is seen as technologically mature, yet its operational performance typically remains sub-optimal, even within the less difficult frontal face detection tests. Empirical evidence shows that the Viola–Jones framework, a standard face detection solution with generally superior performance and other desirable properties, underdetects in some instances. Some true faces survive all but the final stages of the rejection cascade, resulting in missed faces. A hybrid framework consisting of a neural network following a truncated Viola–Jones cascade is constructed in an attempt to recover the undetected faces. Presumably, the neural network could fine tune and augment the face decision. Its inputs are a subset of the thresholding (detection) values of a rejection cascade's intermediate stages. Experiments reveal significantly improved performance, with increased detection rates if no false alarm increases are tolerated, with a greater detection rate increase if some false alarm increases are acceptable, and with a substantial false alarm reduction with no detection reduction. These improved face detection results could address shortcomings in widely-varying applications.

References

    1. 1)
      • 1. Bigdeli, A., Sim, C., Biglari-Abhari, M., et al: ‘Face detection on embedded systems’. Embedded Software and Systems, 2007 (Lecture Notes in Computer Science, 4523), pp. 295308.
    2. 2)
      • 2. Klontz, J.C., Jain, A.K.: ‘A case study of automated face recognition: the Boston Marathon bombings suspects’, IEEE Comput., 2013, 46, (11), pp. 9194.
    3. 3)
      • 3. Zhang, H., Liu, Y., Xie, B., et al: ‘A boosting approach to learning receptive fields for scene categorization’. 2013 20th IEEE Int. Conf. on Image Processing (ICIP), 2013, pp. 265269.
    4. 4)
      • 4. Savakis, A., Stump, M., Tsagkatakis, G., et al: ‘Low vision assistance using face detection and tracking on android smartphones’. 2012 IEEE 55th Int. Midwest Symp. on Circuits and Systems (MWSCAS), 2012, pp. 11761179.
    5. 5)
      • 5. Miyama, M., Matsuda, Y.: ‘Integrated face detection, tracking, and pose estimation’. 2012 IEEE 11th Int. Conf. on Signal Processing (ICSP), 2012, vol. 2, pp. 10561059.
    6. 6)
      • 6. Yao, M., Aoki, K., Nagahashi, H.: ‘Segmentation-based illumination normalization for face detection’. 2013 IEEE Sixth Int. Workshop on Computational Intelligence & Applications (IWCIA), 2013, pp. 95100.
    7. 7)
      • 7. Wesierski, D., Mkhinini, M., Horain, P., et al: ‘Fast recursive ensemble convolution of Haar-like features’. 2012 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 36893696.
    8. 8)
      • 8. Phuong, H.M., Dung, L., de Souza-Daw, T., et al: ‘Extraction of human facial features based on Haar feature with Adaboost and image recognition techniques’. 2012 Fourth Int. Conf. on Communications and Electronics (ICCE), 2012, pp. 302305.
    9. 9)
      • 9. Li, B., Yang, A., Yang, J.: ‘Rotated face detection using AdaBoost’. 2010 Second Int. Conf. on Information Engineering and Computer Science (ICIECS), 2010, pp. 14.
    10. 10)
      • 10. Shen, C., Paisitkriangkrai, S., Zhang, J.: ‘Efficiently learning a detection cascade with sparse eigenvectors’, IEEE Trans. Image Process., 2011, 20, (1), pp. 2235.
    11. 11)
      • 11. Cheng, X., Lakemond, R., Fookes, C., et al: ‘Efficient real-time face detection for high resolution surveillance applications’. 2012 Sixth Int. Conf. on Signal Processing and Communication Systems (ICSPCS), 2012, pp. 16.
    12. 12)
      • 12. Mayank, P., Mukhopadhyay, S.: ‘Temporal correlation and probabilistic prediction based face detection framework in real time environment’. 2012 Fourth Int. Conf. on Intelligent Human Computer Interaction (IHCI), 2012, pp. 16.
    13. 13)
      • 13. Ishii, I., Ichida, H., Takaki, T.: ‘GPU-based face tracking at 500 fps’. 2011 18th IEEE Int. Conf. on Image Processing (ICIP), 2011, pp. 557560.
    14. 14)
      • 14. Erdem, C.E., Ulukaya, S., Karaali, A., et al: ‘Combining Haar feature and skin color based classifiers for face detection’. 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 14971500.
    15. 15)
      • 15. Pham, M.-T., Gao, Y., Hoang, V.-D.D., et al: ‘Fast polygonal integration and its application in extending Haar-like features to improve object detection’. 2010 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 942949.
    16. 16)
      • 16. Dupuis, Y., Savatier, X., Ertaud, J.-Y., et al: ‘Robust radial face detection for omnidirectional vision’, IEEE Trans. Image Process., 2013, 22, (5), pp. 18081821.
    17. 17)
      • 17. Boumbarov, O., Panev, S., Paliy, I., et al: ‘Homography-based face orientation determination from a fixed monocular camera’. 2011 IEEE Sixth Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011, vol. 1, pp. 399403.
    18. 18)
      • 18. Tsalakanidou, F., Malassiotis, S., Strintzis, M.G.: ‘Face localization and authentication using color and depth images’, IEEE Trans. Image Process., 2005, 14, (2), pp. 152168.
    19. 19)
      • 19. Matai, J., Irturk, A., Kastner, R.: ‘Design and Implementation of an FPGA-based real-time face recognition system’. 2011 IEEE 19th Annual Int. Symp. on Field-Programmable Custom Computing Machines (FCCM), 2011, pp. 97100.
    20. 20)
      • 20. Hefenbrock, D., Oberg, J., Thanh, N., et al: ‘Accelerating Viola-Jones face detection to FPGA level using GPUs’. 2010 18th IEEE Annual Int. Symp. on Field-Programmable Custom Computing Machines (FCCM), 2010, pp. 1118.
    21. 21)
      • 21. Yang, M.-H., Kriegman, D.J., Ahuja, N.: ‘Detecting faces in images: a survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (1), pp. 3458.
    22. 22)
      • 22. Zhang, C., Zhang, Z.: ‘A survey of recent advances in face detection’. Technical Report MSR-TR-2010-66, Microsoft Research, Microsoft Corporation, 2010, pp. 117.
    23. 23)
      • 23. OpenCV. http://opencv.org/.
    24. 24)
      • 24. Viola, P., Jones, M.J.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 57, (2), pp. 137154.
    25. 25)
      • 25. Farajzadeh, N., Faez, K.: ‘Hybrid face detection system with robust face and non-face discriminability’. 23rd Int. Conf. Image and Vision Computing New Zealand (IVCNZ), 2008, 2008, pp. 16.
    26. 26)
      • 26. Murphy, T.M., Broussard, R., Schultz, R., et al: ‘A Viola-Jones based hybrid face detection framework’. IS&T SPIE Electronic Imaging 2014, 2014, pp. 111.
    27. 27)
      • 27. Bradski, G., Kaehler, A.: ‘Learning OpenCV: computer vision with the OpenCV library’ (O'Reilly Media, Inc, Sebastopol, CA, 2008).
    28. 28)
      • 28. Freund, Y., Schapire, R.E.: ‘A decision-theoretic generalization of on-line learning and an application to boosting’, J. Comput. Syst. Sci., 1997, 55, (1), pp. 119139.
    29. 29)
      • 29. Ruck, D.W., Rogers, S.K., Kabrisky, M., et al: ‘The multilayer perceptron as an approximation to a Bayes optimal discriminant function’, IEEE Trans. Neural Netw., 1990, 1, (4), pp. 296298.
    30. 30)
      • 30. Cybenko, G.: ‘Approximation by Superpositions of Sigmoidal Functions. Mathematics of Control, Signals, and Systems, 1982.
    31. 31)
      • 31. Broussard, R.P., Ives, R.W.: ‘Using artificial neural networks and feature saliency to identify Iris measurements that contain the most discriminatory information for Iris segmentation’. 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, 2009.
    32. 32)
      • 32. MATLAB – The Language of Technical Computing. http://www.mathworks.com/products/matlab/.
    33. 33)
      • 33. Visual Studio – Home. http://www.microsoft.com/visualstudio/eng/products/visual-studio-2010-express.
    34. 34)
      • 34. CMU/VASC Image Database. http://vasc.ri.cmu.edu/idb.
    35. 35)
      • 35. Rowley, H.A., Baluja, S., Kanade, T.: ‘Neural network-based face detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (1), pp. 2338.
    36. 36)
      • 36. Klette, R., Peleg, S., Sommer, G. (eds.): ‘Robot Vision 2001’ (LNCS, 1998), (Springer, 2001).
    37. 37)
      • 37. Halevy, A., Norvig, P., Pereira, F.: ‘The unreasonable effectiveness of data’, IEEE Intell. Syst., 2009, 24, (2), pp. 812.
    38. 38)
      • 38. Phillips, P.J., Moon, H., Rizvi, S.A., et al: ‘The FERET evaluation methodology for face-recognition algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (10), pp. 10901104.
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