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Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31763–31785 | Cite as

Face detection approach from video with the aid of KPCM and improved neural network classifier

  • A. Vivek Yoganand
  • A. Celine Kavida
  • Rukmanidevi
Article
  • 29 Downloads

Abstract

In recent years, the detection of a human face from the video has become an interesting research topic due to the video surveillance and other security issues. Efficient face detection from the video has become an immense need as it can provide various identity measures in the field of defense and other security-related areas. In our proposed method we have developed an efficient method of face detection to index a particular face from different video shots. The proposed method can be divided into Different modules. In the first module, human face from the video is extracted using segmentation technique. In our proposed method, we have used Kernel-based Possibilistic C-Means for segmentation purpose. The second module in our method is the feature extraction process where shape, LBP, and some geometrical features are extracted. The various shape features like area, circularity, and eccentricity are extracted. Once the feature values are extracted we track the particular face using forward tracking process. After the tracking process, we employ the classification technique. The classifier we utilized here is the improved neural network where the weights factors are optimized using the modified cuckoo search algorithm. The performance is compared with some existing works in order to prove the efficiency of our proposed method.

Keywords

Face detection FCM Neural network 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • A. Vivek Yoganand
    • 1
  • A. Celine Kavida
    • 2
  • Rukmanidevi
    • 3
  1. 1.Department of Computer Science and EngineeringJayam College of Engineering and TechnologyDharmapuriIndia
  2. 2.Department of PhysicsVeltech Multitech College of EngineeringChennaiIndia
  3. 3.Department of Computer Science and EngineeringR.M.D. Engineering CollegeTiruvallurIndia

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