By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay
This edited quantity includes a number of refereed and revised papers initially provided on the foreign Symposium on sign Processing and clever acceptance platforms (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee obtained 134 submissions from eleven nations. each one paper used to be peer reviewed by way of at the least 3 or extra self reliant referees of this system committee and the fifty two papers have been ultimately chosen. The papers supply stimulating insights into development reputation, computing device studying and Knowledge-Based platforms sign and Speech Processing picture and Video Processing cellular Computing and purposes and computing device imaginative and prescient. The ebook is directed to the researchers and scientists engaged in a number of box of sign processing and similar components.
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Additional resources for Advances in Signal Processing and Intelligent Recognition Systems
To show the validity of our assumption, we have illustrated rejection ratio value of different kinds of example test images in figure 2. Based on cross validation, we formulated a decision rule by setting threshold tr1 to decide whether to accept or reject the identity of a given test face. 38 S. Karuppusamy and J. Jerome (a) (b) (c) (d) Fig. 2 Illustration of rejection ratio for various test images (a) Non face (b) Valid face with occlusion (c) Valid face with different expression and (d) New face which is not in gallery If tr1 > φ , test image identity is accepted otherwise that will be rejected and system will start to process next frame.
Jerome (a) (b) (c) (d) Fig. 2 Illustration of rejection ratio for various test images (a) Non face (b) Valid face with occlusion (c) Valid face with different expression and (d) New face which is not in gallery If tr1 > φ , test image identity is accepted otherwise that will be rejected and system will start to process next frame. e. φ > th2 > th1 . After that, we have to find whether test face is occluded or not to avoid partially occluded faces used for corresponding model update. To detect the occlusion on test face, we have used sparse error coefficients ( eopt ) of corresponding identity model.
After that, we have to find whether test face is occluded or not to avoid partially occluded faces used for corresponding model update. To detect the occlusion on test face, we have used sparse error coefficients ( eopt ) of corresponding identity model. Any nonzero coefficient in eopt shows that, corresponding pixel in the test image has been corrupted. Using eopt coefficients, we have defined occlusion ratio η , as ratio of number of non-zero coefficients in eopt to total number of coefficients in eopt .