Papers by mahananda malkauthekar
Journal of Allergy and Clinical Immunology, 2022
We present in this paper, Fourier descriptor and feedforward neural network for face recognition.... more We present in this paper, Fourier descriptor and feedforward neural network for face recognition. Analysis is done for various numbers of iterations. Comparison shows that faces are recognized with FFNN more accurately with 50000 iterations. For experiment, FERET database is used.
2015 International Conference on Communications and Signal Processing (ICCSP), 2015
Minutiae points are the most commonly used as well as accurate feature extraction method of finge... more Minutiae points are the most commonly used as well as accurate feature extraction method of fingerprint recognition system. Using biometrics for person identification is a growing field in many areas, which requires storage of database called template. Biometric system is vulnerable to attack. To reduce this drawback, cryptosystem is combined with biometric. Most common cryptosystem for fingerprint recognition system is fuzzy vault. In this work, two variable polynomial is used to conceal minutiae points, and only encoding is used to decide match/non match result. It gives same result as method which uses encoding and decoding of templates. It reduces computational complexity.
Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), 2013
The face expression recognition problem is challenging because different individuals display the ... more The face expression recognition problem is challenging because different individuals display the same expression differently [1].Here PCA algorithm is used for the feature extraction. Distance metric or matching criteria is the main tool for retrieving similar images from large image databases for the above category of search. Two distance metrics, such as the L1 metric (Manhattan Distance), the L2 metric (Euclidean Distance) have been proposed in the literature for measuring similarity between feature vectors. In content-based image retrieval systems, Manhattan distance and Euclidean distance are typically used to determine similarities between a pair of image [2]. Here facial images of three subjects with different expression and angles are used for classification. Experimental results are compared and the results show that the Manhattan distance performs better than the Euclidean Distance.
2009 IEEE International Advance Computing Conference, 2009
This paper proposes a face recognition method using the FERET face database. Facial images of two... more This paper proposes a face recognition method using the FERET face database. Facial images of two classes and three classes with different expressions and angles are used for classification. Fisher Discriminant Method is used for comparison of the results of two classes with the results of three classes. Euclidian distance method is used for similarity measure. The experimental results have been demonstrated that performance of Fisher Discriminant Analysis for three classes is same as the performance for two classes.
Wireless Personal Communications
2011 International Conference on Emerging Trends in Electrical and Computer Technology, 2011
The face expression recognition problem is challenging because different individuals display the ... more The face expression recognition problem is challenging because different individuals display the same expression differently [1].Here PCA algorithm is used for the feature extraction. Distance metric or matching criteria is the main tool for retrieving similar images from large image databases for the above category of search. Two distance metrics, such as the L1 metric (Manhattan Distance), the L2 metric (Euclidean Distance) have been proposed in the literature for measuring similarity between feature vectors. In content-based image retrieval systems, Manhattan distance and Euclidean distance are typically used to determine similarities between a pair of image [2]. Here facial images of three subjects with different expression and angles are used for classification. Experimental results are compared and the results show that the Manhattan distance performs better than the Euclidean Distance.
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Papers by mahananda malkauthekar