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

Current Issue Vol. 11 Issue 2

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Volume 3 Issue 3 September - November 2016

Research Paper

Implementation of A Dynamic Gesture Recognition Based Indian Sign Language

Pushpendra Patel* , Sandeep B. Patil**

* PG Student, Department of Electronics and Communication Engineering, Shri Shankaracharya Technical Campus of Engineering and Technology, Bhilai, India.

** Associate Professor, Department of Electrical and Electronics Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

Patel, P., and Patil, S. B. (2016). Implementation Of A Dynamic Gesture Recognition Based Indian Sign Language. . i-manager’s Journal on Pattern Recognition, 3(3), 1-6. https://doi.org/10.26634/jpr.3.3.12404

Abstract

In this proposed work, the authors have designed a structure for the accurate hand gesture recognition using MATLAB. Computation based hand classifier is recommended for the dynamic gesture recognition. Now-a-days, it provides platform for those people who are unable to communicate like a normal person. The Principal Component Analysis based algorithm is capable of doing this work. This is done by creating an invariant subspace, which creates a vocabulary in such a manner that any abnormal person can learn and express himself easily. For doing so, using the movement of hands, its shape and position are obtained as an information which can be recognized for the gesture recognition. One of the methods is Hidden Markov Models (HMMs) capable of recognizing these combinations.

References

[1]. Tripathi, K., Baranwal, N., & Nandi, G.C. (2015). “Continuous dynamic Indian Sign Language gesture recognition with invariant backgrounds”. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on IEEE, Kochi, pp.2211- 2216.

[2]. Abid, M.R., Petriu, E.M., & Amjadian, E. (2015). “Dynamic Sign Language Recognition for smart home interactive application using stochastic linear formal grammar”. IEEE Transactions on Instrumentation and Measurement, Vol.64, No.3, pp.596-605.

[3]. Wu, S., & Nagahashi, H. (2013). “Real-time 2D hands detection and tracking for sign language recognition”. In System of Systems Engineering (SoSE), 2013 8 International Conference on IEEE, Maui, HI, pp.40-45.

[4]. Kuznetsova, A., Leal-Taixé, L., & Rosenhahn, B. (2013). “Real-time sign language recognition using a consumer depth camera”. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, NSW, pp.83-90.

[5]. Ong, E.J., Cooper, H., Pugeault, N., & Bowden, R. (2012). “Sign language recognition using sequential pattern trees”. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on IEEE, Providence, RI, pp.2200-2207.

[6]. Mekala, P., Gao, Y., Fan, J., & Davari, A. (2011). “Realtime sign language recognition based on neural network rd architecture”. In System Theory (SSST), 2011 IEEE 43 Southeastern Symposium on IEEE, Auburn, AL, pp.195- 199.

[7]. Yang, R., Sarkar, S., & Loeding, B. (2010). “Handling movement epenthesis and hand segmentation ambiguities in continuous sign language recognition using nested dynamic programming”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.3, pp.462-477.

[8]. Hamada, Y., Shimada, N., & Shirai, Y. (2004). “Hand shape estimation under complex backgrounds for sign language recognition”. In Automatic Face and Gesture Recognition, 2004. Proceedings Sixth IEEE International Conference on IEEE, pp.589-594.

[9]. Marcel, S., Bernier, O., Viallet, J.E., & Collobert, D. (2000). “Hand gesture recognition using input-output hidden markov models”. In Automatic Face and Gesture Recognition, 2000. Proceedings. Four th IEEE International Conference on IEEE, Grenoble, pp.456- 461.

[10]. Rautaray, S.S., & Agrawal, A. (2010). “A novel human computer interface based on hand gesture recognition using computer vision techniques”. In Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, Allahabad, India (ACM), pp.292-296.

[11]. Rautaray, S.S., & Agrawal, A. (2012). “Real time hand gesture recognition system for dynamic applications”. International Journal of UbiComp, Vol.3, No.1, pp.21.

[12]. Liu, N., & Lovell, B. C. (2001). “MMX-accelerated real-time hand tracking system”. In Proceedings of IVCNZ, pp.381-385.

[13]. Chen, F. S., Fu, C. M., & Huang, C. L. (2003). “Hand gesture recognition using a real-time tracking method and Hidden Markov Models”. Image and Vision Computing, Vol.21, No.8, pp.745-758.

[14]. Lee, C., Ghyme, S., Park, C., & Wohn, K. (1998). “The control of avatar motion using hand gesture”. In Proceedings of the ACM symposium on Virtual Reality Software and Technology, Taipei, Taiwan (ACM), pp.59- 65.

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Research Paper

Mouth And Eye Detection Based Drowsy Driver Warning System UsingViola Jones Algorithm

Ankita Thakur* , Sagar Singh Rathore**

PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Associate Professor, Department of Electrical and Instrumentation Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Thakur, A., and Rathore, S. S. (2016). Mouth And Eye Detection Based Drowsy Driver Warning System Using Viola Jones Algorithm. i-manager’s Journal on Pattern Recognition, 3(3), 7-12. https://doi.org/10.26634/jpr.3.3.12405

Abstract

Alertness of the driver is very important to reduce accidents that occur. Sometimes due to long journey, the drivers often get tired and suffer from the problems of sleepiness. So it is important to alert the driver when they are drowsy. In this work, a technique has been developed to monitor the drivers during their whole journey, which is done on the basis of detecting their eyes or body posture. Several methods have been already designed for the detection of drowsiness of the driver. The algorithm is designed using MATLAB. This method includes a sensor or camera which will capture the image and the captured images are continuously monitored and send to the buzzer. The default template has already designed and fed as a reference source, so that the captured image can be easily matched with the reference image.

References

[1]. M. Rizon and T. Kawaguchi, (2003). “Automatic Eye Detection using Intensity and Edge Information”. Proceedings IEEE TENCON, Kuala Lumpur, Vol.2, pp.415- 420.

[2]. Arun, S., Kenneth S., and Murugappan, M. (2012). “Detecting Driver Drowsiness based on Sensors: A Review”. Sensors, Vol.21, No.12, pp.16937-16953.

[3]. Shabnam Abtahi, Shervin Shirmohammadi, Behnoosh Hariri, Daniel Laroche, and Luc Martel, (2013). “A yawning measurement method using embedded smart cameras”. Instrumentation and Measurement Technology Conference (I2MTC) 2013 IEEE International, Minneapolis, MN, pp.1605-1608.

[4]. M.J. Flores, M. Anningll, and A. Escalera, (2009). “Real-Time Warning System for Driver Drowsiness Detection using Visual Information”. J. Intell. Robot System, Vol.59, No.2 , pp.103 -125.

[5]. Viola, and Jones, (2001). “Robust Real-time Object Detection”. IJCV, Vol.57, No.2, pp.137-154.

[6]. C. Zhang, X. Lin, R. Lu, P.H. Ho, and X. Shen, (2008). “An efficient message authentication scheme for vehicular communications”. IEEE Trans. Veh. TechnoI., Vol.57, No.6, pp.3357-3368.

[7]. Charles, C., Simon, G., & Michael, G., (2009). “Predicting Driver Drowsiness using Vehicle Measures: Recent Insights and Future Challenges”. Journal of Safety Research, Vol.40, No.2, pp.239-45.

[8]. G. Hosseini, and H. Hossein-Zadeh, A. (2006). “Display driver drowsiness warning system”. International Conference of the Road and Traffic Accidents, Tehran University.

[9]. L. M. Bergasa, and J. Nuevo, (2006). “Real-Time system for monitoring driver vigilance”. IEEE Transactions on Intelligent Transportation Systems, Vol.7, No.1, pp.63- 77.

[10]. P. Gejgus, and M. Sparka, (2003). “Face Tracking in Color Video Sequences”. SCCG’03 Proceedings of the 19 Spring Conference on Computer Graphics, Budmerice, Slovakia, pp.245-249.

[11]. R G Gonzales, and RE. Woods, (2002). Digital Image Processing, Second Edition. Prentice Hall.

[12]. Das, D., Shiyu, Z., & Lee, J. (2012). “Differentiating Alcohol-Induced Driving Behavior using Steering Wheel Signals”. Intelligent Transportation Systems, IEEE Transactions on, Vol.13, No.3, pp.1355-1368.

[13]. Hamzah S. AlZu'bi, Waleed Al-Nuaimy, and Nayel S. Al-Zubi, (2013). “EEG-based Driver Fatigue Detection”. IEEE Sixth International Conference on Developments in eSystems-Engineering (DeSE), pp.111-114.

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Research Paper

Static Devnagari Sign Language Recognition

Versha Verma* , Sandeep B. Patil**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Associate Professor, Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Verma, V., and Patil, S. B. (2016). Static Devnagari Sign Language Recognition. i-manager’s Journal on Pattern Recognition. i-manager’s Journal on Pattern Recognition, 3(3), 13-18. https://doi.org/10.26634/jpr.3.3.12406

Abstract

Hand gesture based sign language is a different way of communication among the Deaf-mute and physically impaired people performed using specific hand gestures. Deaf-mute people face struggle in expressing their feelings to other people, which creates a communication gap between normal and deaf-mute people. This paper, based on hand gesture based Devnagari Sign language Recognition approaches aim to provide a communication way for the Deafmute Community over the society. Therefore, the authors have used static hand gesture based sign language recognition for Deaf-mute communication system. Many researchers use only single American Sign Language or Indian Sign Language for creating their database. In this paper, recent research of sign language is reviewed based on manual communication and body language. A hand gesture based Devnagari Sign Language for recognizing Hindi characters using hand gestures of Deaf-mute people is developed. Devnagari Sign Language recognition system is typically explained with five steps, i.e. acquisition, segmentation, pre-processing, feature extraction, and classification.

References

[1]. J. L. Hernandez-Rebollar, N. Kyriakopoulos, and R. W. Linderman, (2004). “A New Instrumented Approach for Translating American Sign Language into Sound and Text”. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.547-552.

[2]. D. Stein, P. Dreuw, H. Ney, S. Morrissey, and A. Way, (2007). “Hand in Hand Automatic Sign Language to English Translation”. Proceedings of the 11 Conference on Theoretical and Methodological Issues in Machine Translation, StÖvde, Sweden, pp.214-220

[3]. N. Tanibata, N. Shimada, and Y. Shirai, (2002). “Extraction of Hand Features for Recognition of Sign Language Words”. In Proceedings of the 15 International Conference on Vision Interface, pp.391-398.

[4]. D. J. Rios-Soria, S. E. Schaeffer, and S. E. Garza- Villarreal, (2013). “Hand-gesture recognition using computer-vision techniques”. In 21 International Conference on Computer Graphics, Visualization and Computer Vision, Czech Republic, pp.1-8.

[5]. J. Singha and K. Das, (2013). “Hand Gesture Recognition Based on Karhunen-Loeve Transform”. In Mobile & Embedded Technology International Conference (MECON), pp.365-371.

[6]. K. K. Safaya and P. J.W.Bakal, (2013). “Real Time Based Bare Hand Gesture Recognition”. IPASJ International Journal of Information Technology (IIJIT), Vol.1, No.2, pp.1-9.

[7]. P. Chaudhary and H.S. Ryait, (2014). “Neural Network Based Static Sign Gesture”. International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, No.2, pp.3066-3072.

[8]. J. R. Pansare, S. H. Gawande, and M. Ingle, (2012). “Real-time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background”. Journal of Signal and Information Processing, Vol.3, No.3, pp.364- 367.

[9]. S. Pramada, D. Saylee, N. Pranita, N. Samiksha, and M.S. Vaidya, (2013). “Intelligent Sign Language Recognition using Image Processing”. IOSR Journal of Engineering (IOSRJEN), Vol.3, No.2, pp.45-51.

[10]. C. Evans, (2009). Notes on the OpenSURF Library. University of Bristol, Department of Computer Vision.

[11]. H. Bay, T. Tuytelaars, and L. V. Gool, (2006). “SURF: Speeded up Robust Features”. In 9 European Conference on Computer Vision, pp.404-417.

[12]. M. A. Rahman, Ahsan-Ui-Ambia and M. Aktaruzzaman, (2011). “Recognition Static Hand Gesture of Alphbetin ASL”. IJCIT, Vol.2, No.1, pp.75-78.

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Research Paper

An Improved Diamond Search Pattern For Motion Estimation

Sheetal Banchhor* , Dolley Shukla**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

** Associate Professor, Department of Information Technology, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

Banchhor, S., and Shukla, D. (2016). An Improved Diamond Search Pattern For Motion Estimation. i-manager’s Journal on Pattern Recognition, 3(3), 19-23. https://doi.org/10.26634/jpr.3.3.12407

Abstract

One of the most widely accepted search patterns for motion estimation is Diamond Search (DS). DS patterns formed are better than Full Search, Three step search, New Three Step Search, etc., but over the years, many other hybrid patterns, such as Hexagon Diamond, Cross Diamond Search, Modified Small Cross Diamond Search, etc., have been developed. These hybrid algorithms outperform DS either in terms of PSNR or search pixel. A new Improved Diamond Search pattern has been proposed in this paper. Improved DS is center biased. Changes in the search direction within the conventional DS have been made to introduce the Improved DS. This paper performs a comparison between the DS and Improved DS by implementing them in the different pixel space of the block of the frame. The test result gives PSNR better in case of Improved DS than that of DS showing that Improved DS is more efficient than the DS.

References

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[10]. R.A. Manap, S.S.S. Ranjit, A.A. Basari, and B. H. Ahmad, (2010). “Performance Analysis of Hexago Diamond Search Algorithm for Motion Estimation”. In 2 International Conference on Computer Engineering and Technology, Chengdu, Vol.3, pp.156-159.

[11]. Hongjun Jia, and Li Zhang, (2008). “Directional Cross Diamond Search Algorithm for Fast Block Motion Estimation”. arXiv preprint arXiv:0806.0689.

[12]. Shiping Zhua, Jun Tiana, Xiaodong Shena, and Kamel Belloulatab, (2009). “A New Cross-Diamond Search Algorithm for Fast Block Motion Estimation”. 16 IEEE International Conference on Image Processing (ICIP), Cairo, pp.1581-1584

[13]. Karam Singh and Shaik Rafi Ahamed, (2013). “Modified Small-Cross Diamond Search Motion Estimation Algorithm for H.264/AVC”. In Computer, Communications, and Control Technology (I4CT), 2014 International Conference on, IEEE, pp.467-471.

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Research Paper

Estimation and Reduction of Gaussian Noise and High-Density Salt and Pepper Noise Through Modified Decision Based Trimmed Median and Gaussian Filter Using Matlab

Apoorwa Tiwari* , Devanand Bhonsle**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

** Senior Assistant Professor, Department of Electrical & Electronics, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

Tiwari, A., and Bhonsle, D. (2016). Estimation And Reduction Of Gaussian Noise And High-Density Salt And Pepper Noise Through Modified Decision Based Trimmed Median And Gaussian Filter Using Matlab. i-manager’s Journal on Pattern Recognition, 3(3), 24-31. https://doi.org/10.26634/jpr.3.3.12408

Abstract

In this paper, the authors have proposed a novel approach for noise estimation and reduction from a degraded image. The algorithm is designed using modified decision based trim median filter. This method helps to estimate the actual salt and pepper noise in any degraded image. They have also calculated the presence of Gaussian noise in some noisy images. In this paper, two filters were used; one is trim median filter used for estimation and removal of noise from the image corrupted with Salt and Pepper noise and the other is Gaussian filter which is used for Gaussian noise estimation and reduction. This is a better algorithm because it is based on a modified decision based system. GUI model has been used for this system which helps us to control and estimate the above mentioned noises in an image.>

References

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Review Paper

A Review on The Comparison of Global Features Based Techniques LDA, PCA, and LBP Algorithm for Face Recognition

Rachana Dewangan* , Swati Verma**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Dewangan, R., and Verma, S. (2016). A Review On The Comparison Of Global Features Based Techniques Lda, Pca, And Lbp Algorithm For Face Recognition. i-manager’s Journal on Pattern Recognition, 3(3), 32-38. https://doi.org/10.26634/jpr.3.3.12409

Abstract

Nowadays, face recognition has become very much popular to recognize a person with their face in order to avoid crime, etc. This mechanism is based on the division of the face, processing into three phases, i.e. face detection, feature extraction based on the input face image, and face recognition. In this paper, the authors have compared three algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Patterns (LBP). The rate of accuracy of face recognition has also been compared. The advantages and disadvantages of these algorithms will help in obtaining a solution, so that a better face recognition system can be designed.

References

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

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 3 Issue 3 September - November 2016

Implementation of A Dynamic Gesture Recognition Based Indian Sign Language

Pushpendra Patel* , Sandeep B. Patil**

Patel, P., and Patil, S. B. (2016). Implementation Of A Dynamic Gesture Recognition Based Indian Sign Language. . i-manager’s Journal on Pattern Recognition, 3(3), 1-6. https://doi.org/10.26634/jpr.3.3.12404

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Mouth And Eye Detection Based Drowsy Driver Warning System UsingViola Jones Algorithm

Ankita Thakur* , Sagar Singh Rathore**

** PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India. ** Associate Professor, Department of Electrical and Instrumentation Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Thakur, A., and Rathore, S. S. (2016). Mouth And Eye Detection Based Drowsy Driver Warning System Using Viola Jones Algorithm. i-manager’s Journal on Pattern Recognition, 3(3), 7-12. https://doi.org/10.26634/jpr.3.3.12405

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Static Devnagari Sign Language Recognition

Versha Verma* , Sandeep B. Patil**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India. ** Associate Professor, Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Verma, V., and Patil, S. B. (2016). Static Devnagari Sign Language Recognition. i-manager’s Journal on Pattern Recognition. i-manager’s Journal on Pattern Recognition, 3(3), 13-18. https://doi.org/10.26634/jpr.3.3.12406

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An Improved Diamond Search Pattern For Motion Estimation

Sheetal Banchhor* , Dolley Shukla**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India. ** Associate Professor, Department of Information Technology, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

Banchhor, S., and Shukla, D. (2016). An Improved Diamond Search Pattern For Motion Estimation. i-manager’s Journal on Pattern Recognition, 3(3), 19-23. https://doi.org/10.26634/jpr.3.3.12407

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Estimation and Reduction of Gaussian Noise and High-Density Salt and Pepper Noise Through Modified Decision Based Trimmed Median and Gaussian Filter Using Matlab

Apoorwa Tiwari* , Devanand Bhonsle**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India. ** Senior Assistant Professor, Department of Electrical & Electronics, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

Tiwari, A., and Bhonsle, D. (2016). Estimation And Reduction Of Gaussian Noise And High-Density Salt And Pepper Noise Through Modified Decision Based Trimmed Median And Gaussian Filter Using Matlab. i-manager’s Journal on Pattern Recognition, 3(3), 24-31. https://doi.org/10.26634/jpr.3.3.12408

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A Review on The Comparison of Global Features Based Techniques LDA, PCA, and LBP Algorithm for Face Recognition

Rachana Dewangan* , Swati Verma**

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India. ** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Dewangan, R., and Verma, S. (2016). A Review On The Comparison Of Global Features Based Techniques Lda, Pca, And Lbp Algorithm For Face Recognition. i-manager’s Journal on Pattern Recognition, 3(3), 32-38. https://doi.org/10.26634/jpr.3.3.12409

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PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

Associate Professor, Department of Electrical and Instrumentation Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Associate Professor, Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

** Associate Professor, Department of Information Technology, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

** Senior Assistant Professor, Department of Electrical & Electronics, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.

* PG Student, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.

** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.