By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay
This edited quantity encompasses a collection of refereed and revised papers initially provided on the overseas Symposium on sign Processing and clever acceptance structures (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee acquired 134 submissions from eleven international locations. every one paper was once peer reviewed by means of at the least 3 or extra self reliant referees of this system committee and the fifty two papers have been eventually chosen. The papers supply stimulating insights into trend attractiveness, desktop studying and Knowledge-Based structures sign and Speech Processing photograph and Video Processing cellular Computing and functions and machine imaginative and prescient. The e-book is directed to the researchers and scientists engaged in numerous box of sign processing and similar components.
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Extra info for Advances in Signal Processing and Intelligent Recognition Systems
I =1 i The objective of FLD is to find the direction in which between-Class scatter (Sb) is maximized and within-Class scatter (Sw) is minimized as given in (3). J fld = T arg max W S b W W W TS W w (3) But in practice, due to few available samples and high dimensionality, Sw becomes singular. To overcome this limitation, some variants of FLD are proposed in literature as discussed next. Fisherfaces Fisherfaces  is a popular variant of FLD method. In this method, first the dimensionality of the data samples is reduced using PCA and then FLD criterion is applied in the reduced dimension for feature extraction.
The results clearly show the advantage of block based feature extraction over whole image based. 3 Discussion In this work, distance calculation for classification is carried out without considering the variations in the feature values. When features are combined, better results can be obtained with normalized feature vector. Feature reduction methods like PCA can also be applied on combined features. Improved results with block based images can be attributed to the fact that local features are more discriminating than Ear Recognition Using Texture Features – A Novel Approach 11 global features of non - blocked images.
448–453 (1995) 16. : A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics 8(1), 43–48 (2010) 17. : Toward a universal law of generalization for psychological science. Science 237, 1317–1323 (1987) 18. : Face recognition: A literature survey. K. Agrawal, and Ajay Jaiswal Abstract. In the literature, the performance of Fisher's Linear Discriminant (FLD), Linear Regression (LR) and their variants is found to be satisfactory for face recognition under illumination variation.
Advances in Signal Processing and Intelligent Recognition Systems by Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay
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