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

By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay

This edited quantity incorporates a choice of refereed and revised papers initially awarded on the overseas Symposium on sign Processing and clever reputation structures (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee bought 134 submissions from eleven international locations. each one paper was once peer reviewed by means of at the very least 3 or extra self sufficient referees of this system committee and the fifty two papers have been ultimately chosen. The papers provide stimulating insights into development popularity, 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 publication is directed to the researchers and scientists engaged in quite a few box of sign processing and similar components.

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1007/978-3-319-04960-1_4, © Springer International Publishing Switzerland 2014 33 34 S. Karuppusamy and J. , which are commonly encountered in videos. 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.

EFLD performs best among all the methods on all the datasets. 2. The performance of various methods in general increases or remains same with the increase in number of training images per person. 3. There is less variation in classification accuracy among LR, Fisherfaces and their variants on JAFFE and FEEDTUM datasets. 4. The performance of FLD is worst for all the datasets. 5. The variation in performance of LR based methods is less in comparison to FLD based methods. 5 Conclusion In literature, FLD, LR and their many variants are proposed for face recognition under illumination and pose variations.

Jaiswal Robust Regression Robust Regression [11] plays an important role when the issues of contamination of test images by variations such as expression variation in facial images of different people are present. e. r j (δˆ i ) = y − X i δˆ i , i = 1, 2, …, C and ρ is a symmetric function having minimum at zero. Once the regression coefficients are determined, the rest of the method follows similar procedure as LRC. e. ORL [13], JAFFE [9] and FEEDTUM [16]. The details of the datasets are summarized in Table 1.

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