Efficiency Of Recognition Systems Research Articles

Digitization Of Tulu Handwritten Scripts- A Literature Survey

Applications for handwritten script digitization include automated form processing, archival preservation, document digitization, handwriting recognition software, and historical document research. The performance of the recognition algorithms, the complex nature of the script, and the handwriting quality all affect how accurate the digitization is. Significant research on handwritten text recognition in scripts like Latin, Chinese, Arabic, and Japanese is reported. Still in its infancy, the work described on handwritten Indic scripts necessitates extensive research in this area. This thorough analysis offers an open view of different feature extraction and classification methods for the offline and online identification of handwritten Indic scripts.The primary objective of this paper is to explore the accuracy and efficiency of recognition systems by analyzing several state-of-the-art studies and comparing and evaluating various techniques for Tulu script handwritten character recognition. This paper aims to describe various advancements reported over the last few decades in the field of handwritten scripts recognition.

Comparison and a neural network approach for iris localization

Abstract This paper presents a fast and reliable approach for the localization of an iris image using neural networks by comparing some of the existing localization methods. Normally any biometric recognition system is used for identification of an individual and verified with the available database to check whether the person is authorized or not. Nowadays, iris recognition systems are considered the best authentication method compared to other biometric systems due to the unique characteristic feature of the human iris. In iris recognition systems, the important and difficult step is to locate or segment the iris from the input eye image which is responsible for success rate of the iris recognition. Hence this paper suggests a efficient approach to locate the iris using neural networks in order to improve the efficiency of recognition systems. Further, the paper compares the existing iris localization methods such as Daugman algorithm, Hough transform and Canny edge detector algorithm. The best localization algorithm is chosen for training a network and simulating for efficient and fast segmentation of irises with good success rate.

POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.