Download Advances in Signal Processing and Intelligent Recognition by Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay PDF

By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay

This edited quantity encompasses a number of refereed and revised papers initially offered on the overseas Symposium on sign Processing and clever acceptance platforms (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee bought 134 submissions from eleven nations. every one paper was once peer reviewed through not less than 3 or extra autonomous referees of this system committee and the fifty two papers have been ultimately chosen. The papers provide stimulating insights into development reputation, laptop studying and Knowledge-Based platforms sign and Speech Processing picture and Video Processing cellular Computing and functions and computing device imaginative and prescient. The booklet is directed to the researchers and scientists engaged in a number of box of sign processing and similar components.

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2009) are considered as most representative methods. In LRC, the test image is represented using class specific linear subspace models by least squares estimation method. The face is identified based on distance between test image and reconstructed image. , (2010), which divides test image into multiple nonoverlapping parts. However, if the contiguous occlusion appears on many parts, this method fails. On other hand, SRC based method is proven to be very robust against noise and occlusion. , (2013).

Cogn. Neuro. 3, 71–86 (1991) 16. : Facial expressions and emotion database. html 17. : Generalized discriminant analysis: a matrix exponential approach. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 40, 186–197 (2010) Real-Time Video Based Face Identification Using Fast Sparse Representation and Incremental Learning Selvakumar Karuppusamy and Jovitha Jerome Abstract. Video based face identification is a challenging problem as it needs to learn a robust model to account for face appearance change caused by pose, scale, expression and illumination variations.

They also require large number of training images for better recognition performance which is not possible for real world applications. , 2009) are considered as most representative methods. In LRC, the test image is represented using class specific linear subspace models by least squares estimation method. The face is identified based on distance between test image and reconstructed image. , (2010), which divides test image into multiple nonoverlapping parts. However, if the contiguous occlusion appears on many parts, this method fails.

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