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i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Cited

Volume 4 Issue 3 September - November 2017

Research Paper

Multimodal Biometrics: Most Appropriate for Person Identification

Dr. Snehlata*

*Associate Professor, MATS School of IT, MATS University, Raipur (CG), India.

Barde, S. (2017). Multimodal Biometrics: Most Appropriate for Person Identification. i-manager’s Journal on Pattern Recognition, 4(3), 1-8. https://doi.org/10.26634/jpr.4.3.13881

Abstract

Biometrics is an important application of digital image processing of biometric modalities. There are several types of biometric security technologies such as face recognition, iris recognition, fingerprint matching etc. However, there are some prominent challenges in all biometrics dealing with single modality such as reliability, robustness etc. Therefore, multimodal biometrics is recommended, which involves more than one modality in the system. AADHAAR card is an example of multimodal biometrics.

Multimodal biometrics aims at increasing the reliability of biometric systems. In this research, four traits such as face, ear, iris and foot were used and worked on matching score level and decision level to get advantage of the multimodal approaches. The work was tested over self created database of 100 persons. Different classifier approaches for different modalities were applied; principle component analysis for face, eigen images for ear, hamming distance based technique for iris and modified sequential Haar transform for foot traits. Each biometric trait processed its information independently to calculate weights of individuals. The fusion scheme was applied on all possible combination of traits and the matching score was calculated. The integrated results of different biometric traits increased the recognition performance of the multimodal biometric system considerably

References

[1]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014a). Multimodal Biometrics using Face, Ear and Iris Modalities. International Journal of Computer Applications (IJCA), Recent Advances in Information Technology NCRAIT, (2), 9-15.

[2]. Barde, S. & Zadgaonkar, A. S. (2014b). Ear and Foot Biometrics Best for Identification. International Journal of Novel Research in Computer Science and Software Engineering,1(1), 11-19.

[3]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014c). PCA based Multimodal Biometrics using Ear and Face Modalities. International Journal of Information Technology and Computer Science(IJITCS), 6(5), 43-49.

[4]. Barde S. (2015). Person Identification using Face and Foot Modalities. International Journal of Advanced Engineering Research and Studies (IJAERS), 7(2) 160-163.

[5]. Barde, S. (2016). Person Identification using Ear Modalities. International Journal of Modern Trends in Engineering and Research (IJMTER), 3(5), 8-13.

[6]. Bigun, J., Fierrez-Aguilar, J., Ortega-Garcia, J., & Gonzalez-Rodriguez, J. (2005). Combining biometric evidence for person authentication. Lecture Notes in Computer Science (LNCS), 3161, 1-18.

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[8]. Linas, J., Bowman, C., Rogova, G., Steinberg, A., Waltz, E., & White, F. (2004). Revisiting the JDL data fusion model II. In proc. of 7 International Conference on Information Fusion, Stockholm, Sweden.

[9]. Rattani, A., Kisku, D. R., & Bicego, M. (2010). Feature level fusion of face and fingerprint biometrics. In proc. of 1 IEEE International Conference on Biometrics: Theory, Applications and Systems, (pp. 1-6), IEEE.

[10]. Ross, A. & Jain, A. K. (2004). Multimodal biometrics: An overview. In Signal Processing Conference (EUSIPCO), 12 European (pp.1221-1224). IEEE.

[11]. Ross, A. & Jain, A. K. (2003). Information fusion in biometrics. Pattern Recognition Letters, 24(13), 2115 - 2125.

[12]. Sanderson, C. & Paliwal, K. K. (2001). Information fusion for robust speaker verification. In proc. of Seventh European Conference on Speech Communication and Technology, Aalbo: 755-758.

[13]. Snelick, R. Uludag, U. Mink, A., Indovina, M., & Jain. A. (2005). Large scale evaluation of multimodal biometric authentication using state-of the-art systems, IEEE Transactions on Pattern Analysis and Machine Intelligence., 27(3), 450-455.

[14]. Yao, Y., Jing, X., & Wong, H. S. (2007). Face and palmprint feature level fusion for single sample biometric recognition. Nerocomputing. 70(7), 1582 -1586.

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Research Paper

Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security

Cheluri Trivikram* , Susanna Samarpitha , K. Madhavi*, Diana Moses****

- UG Scholar, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.

Assistant Professor, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.

**** Professor, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.

Trivikram, C., Samarpitha, S., Madhavi, K., and Moses, D. (2017). Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security. i-manager’s Journal on Pattern Recognition, 4(3), 9-16. https://doi.org/10.26634/jpr.4.3.13885

Abstract

Biometric identification is a mandatory tool to secure digital information for various industrial, government, commercial, and security applications. Face recognition is a distinct problem and lacks a unique solution applicable to all situations. Face recognition is not effective in identifying individuals in conditions, when a person is using glasses, hats or has a beard etc. Alternative technologies like Iris and retinal scan biometric techniques need sophisticated equipment, which is not financially viable for all applications. Voice recognition methods have low accuracy and are affected by situations where a change in a person's voice due to illness like cold render absolute identification inaccurate. This paper proposes a biometric method implementing multiple techniques i.e., both face and voice recognition technique as an effective identification tool. Identification process using combined biometric methods makes a foolproof security system, thereby leaving no scope for error. A comprehensive assessment of the performance accuracy of several algorithms like Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLD), Dynamic Time Warping (DTW), Support Virtual Machines (SVM), Neural Networks (NN) implemented in face and voice recognition were studied and then corresponding performance accuracy was reviewed. A new technique has been proposed using these performance accuracy results which would help provide the best hybrid method. The proposed hybrid biometric identification method is a viable solution for industries or areas with need for high security of their data systems.

References

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Research Paper

A Reconstruction of Gray Scale Image into RGB Color Space Image Using YCbCr Color Spacing and Luminance Mapping in Matlab

Nidhi Gupta* , Dolley Shukla**

* Research Scholar, Department of Electronics and Telecommunication Engineering, Faculty of Engineering and Technology, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

** Associate Professor, Department of Information Technology, Faculty of Engineering and Technology, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Gupta, N., and Shukla, D. (2017). A Reconstruction of Gray Scale Image into RGB Color Space Image Using YCbCr Color Spacing and Luminance Mapping in Matlab. i-manager’s Journal on Pattern Recognition, 4(3), 17-26. https://doi.org/10.26634/jpr.4.3.13886

Abstract

RGB (Red, Green, Blue) color scale image to Gray scale image color conversion is very useful and application oriented in the digital image processing world. In this paper, the authors have presented a basic method for the grayscale images to color image conversion by transferring the color elements between a source input, RGB color image and a destination, the gray scale image. The methods compared here are based on matching luminance and color information between the gray images.

The de-corrected color space of image is chosen to provide color to the pixel gray image, which is a basic and simple algorithm. Thus, the color-space with de-correlated technique is an extremely vital tool for manipulating the pixel of color images during this work. Colorization of the gray scale image pixel is obtained here by matching the texture options of gray image with coloring options of the windows of the colored image elements, then mean and variance are imposed on the info points during this simple operation. Thus the required output images are obtained for the acceptable input images.

The gray-scale image pixel and reference-colored image pixel uses YCbCr (Y-Luma Component; Cb, Cr - Bluedifference and Red - Difference Chroma Components) pixel space for the processing of the image pixels in this elements of color-space component data, which is for the colorization of the gray-scale pixel image. The output of system is obtained by colorization using a popular method of using the mean and standard deviation technique in lab. All the methods utilized in this gray-scale image colorization was an automatic image colorization process, requiring two factors of the images i.e. gray-scale image as source and pixel of color image layers as the destination of image. After undergoing histogram analysis, the colors of the source image elements are successfully transferred to the destination image. The closer the luminance image pixel data of both images, the easier the transfer process becomes.

References

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Research Paper

Digit Recognition using TensorFlow Tool

K. V. N. Rajesh* , K.V.N. Ramesh, M. Hymavathi *, K. Syam Sundar Reddy****

* HOD, Department of Electronics and Computer Engineering, Vignan's Institute of Information Technology, Visakhapatnam, India.

Technical Architect, Tech Mahindra, Visakhapatnam, India.

*-**** UG Scholar, Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, India.

Rajesh, K. V. N., Ramesh, K. V. N., Hymavathi, M., and Reddy, K. S. S. (2017). Digit Recognition using TensorFlow Tool. i-manager’s Journal on Pattern Recognition, 4(3), 27-31. https://doi.org/10.26634/jpr.4.3.13887

Abstract

Deep learning and Neural Network algorithms are a branch of Machine learning that can automatically identify patterns in the data, and then use the uncovered patterns to predict future data, or to perform other alternative kinds of decision making under unreliability. Neural Networks is a method of computing, which is based on a collection of large number of neural units, which acts as a biological brain and solve problems with large clusters of biological neurons connected by axons. Deep Learning algorithms are used to model high level abstractions in data. Digit Recognition is a combination of Deep Learning and Neural Network algorithms, which uses TensorFlow tool as an interface to develop a model. This paper describes the recognition of handwritten scanned digits by a system and displays the output as digital numbers by using Machine Learning methods with the help of TensorFlow tool.

References

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Review Paper

Understanding Hindsight, Insight and Forsight Data To Large-Scale Distributed Data Intelligence (Algorithms) Machine: A Scale-Out Review

Sabibullah Mohamed Hanifa*

*Associate Professor & Dean, PG and Research Department of Computer Science, SCAS, Pudukkottai, Tamilnadu, India.

Hanifa, S. M. (2017). Understanding Hindsight, Insight and Forsight Data To Large-Scale Distributed Data Intelligence (Algorithms) Machine: A Scale-Out Review. i-manager’s Journal on Pattern Recognition, 4(3), 32-43. https://doi.org/10.26634/jpr.4.3.13888

Abstract

This review attempts to comprehend the insights and foresights into the grasping power of data science, data quality, data process, data pre-process, big data, big data process and analysis, analytics, BD (Big Data) and analytics lifecycle, file storage, platforms/technologies supported, Hadoop concepts, eco-system components, and design principles. Principle and philosophy behind computations are also explained through flow diagram. Various analytics based on its solutions, cluster computing based platforms like Apache Spark (its architecture – core, other components, and utilities), MLlib package – Machine Learning (ML) methods/ tasks and detailed supported algorithms are exclusively elucidated, to understand the concepts of these pinpoints. The explored comprehensive contents would definitely be useful and provide core understanding knowledge in the large scale ML dependent algorithms process, suitable to build the relevant application solutions (may be predictions/ classifications/ segmentations/ recommendations) via Apache – Spark environment.

References

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i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 4 Issue 3 September - November 2017

Multimodal Biometrics: Most Appropriate for Person Identification

Dr. Snehlata*

*Associate Professor, MATS School of IT, MATS University, Raipur (CG), India.

Barde, S. (2017). Multimodal Biometrics: Most Appropriate for Person Identification. i-manager’s Journal on Pattern Recognition, 4(3), 1-8. https://doi.org/10.26634/jpr.4.3.13881

Abstract

References

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Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security

Cheluri Trivikram* , Susanna Samarpitha **, K. Madhavi***, Diana Moses****

Trivikram, C., Samarpitha, S., Madhavi, K., and Moses, D. (2017). Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security. i-manager’s Journal on Pattern Recognition, 4(3), 9-16. https://doi.org/10.26634/jpr.4.3.13885

Abstract

References

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A Reconstruction of Gray Scale Image into RGB Color Space Image Using YCbCr Color Spacing and Luminance Mapping in Matlab

Nidhi Gupta* , Dolley Shukla**

Gupta, N., and Shukla, D. (2017). A Reconstruction of Gray Scale Image into RGB Color Space Image Using YCbCr Color Spacing and Luminance Mapping in Matlab. i-manager’s Journal on Pattern Recognition, 4(3), 17-26. https://doi.org/10.26634/jpr.4.3.13886

Abstract

References

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Digit Recognition using TensorFlow Tool

K. V. N. Rajesh* , K.V.N. Ramesh**, M. Hymavathi ***, K. Syam Sundar Reddy****

Rajesh, K. V. N., Ramesh, K. V. N., Hymavathi, M., and Reddy, K. S. S. (2017). Digit Recognition using TensorFlow Tool. i-manager’s Journal on Pattern Recognition, 4(3), 27-31. https://doi.org/10.26634/jpr.4.3.13887

Abstract

References

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Understanding Hindsight, Insight and Forsight Data To Large-Scale Distributed Data Intelligence (Algorithms) Machine: A Scale-Out Review

Sabibullah Mohamed Hanifa*

*Associate Professor & Dean, PG and Research Department of Computer Science, SCAS, Pudukkottai, Tamilnadu, India.

Hanifa, S. M. (2017). Understanding Hindsight, Insight and Forsight Data To Large-Scale Distributed Data Intelligence (Algorithms) Machine: A Scale-Out Review. i-manager’s Journal on Pattern Recognition, 4(3), 32-43. https://doi.org/10.26634/jpr.4.3.13888

Abstract

References

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Cheluri Trivikram* , Susanna Samarpitha , K. Madhavi*, Diana Moses****

K. V. N. Rajesh* , K.V.N. Ramesh, M. Hymavathi *, K. Syam Sundar Reddy****